# Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Testing suite for the PyTorch Whisper model.""" import copy import inspect import os import random import re import tempfile import time import unittest import numpy as np import pytest from huggingface_hub import hf_hub_download from parameterized import parameterized from transformers import WhisperConfig from transformers.testing_utils import ( Expectations, is_flaky, require_read_token, require_torch, require_torch_accelerator, require_torch_fp16, require_torch_multi_accelerator, require_torchaudio, slow, torch_device, ) from transformers.utils import is_torch_available, is_torch_xpu_available, is_torchaudio_available from transformers.utils.import_utils import is_datasets_available from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_datasets_available(): import datasets from datasets import Audio, load_dataset if is_torch_available(): import torch from transformers import ( WhisperFeatureExtractor, WhisperForAudioClassification, WhisperForCausalLM, WhisperForConditionalGeneration, WhisperModel, WhisperProcessor, set_seed, ) from transformers.generation import ( GenerateEncoderDecoderOutput, ) from transformers.generation.logits_process import LogitsProcessor from transformers.models.whisper.modeling_whisper import WhisperDecoder, WhisperEncoder, sinusoids class DummyTimestampLogitProcessor(LogitsProcessor): """This processor fakes the correct timestamps tokens pattern [TOK_1] [TOK_2] ... [TOK_N] [TIME_STAMP_TOK_1] [TIME_STAMP_TOK_2] [TOK_N+1] ...""" def __init__( self, timestamp_begin, vocab_size, batch_size, max_length, min_space=3, seed=0, is_length_ascending=True ): self.timestamp_begin = timestamp_begin self.vocab_size = vocab_size self.min_space_between_timestamps = min_space self.timestamp_tokens = torch.arange(self.timestamp_begin, self.vocab_size) self.timestamp_tokens.to(torch_device) self.is_length_ascending = is_length_ascending self.no_time_stamp_counter = batch_size * [0] self.prev_highest_timestamp = batch_size * [0] self.batch_size = batch_size self.max_length = max_length self.count = 0 self.begin_index = 0 self.let_pass = [[] for _ in range(batch_size)] for k in range(batch_size): random.seed(seed + k) for _ in range(10000): self.let_pass[k].append(random.randint(1, 10) <= 3) def set_begin_index(self, begin_index: int): self.begin_index = begin_index def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: # we don't want to randomly sample timestamp tokens if input_ids.shape[-1] != self.begin_index: scores[:, self.timestamp_begin :] = -float("inf") self.no_time_stamp_counter = [x + 1 for x in self.no_time_stamp_counter] for k in range(input_ids.shape[0]): # make sure to use correct index if a batch was removed if self.is_length_ascending and input_ids.shape[0] < self.batch_size: prev_k = k + self.batch_size - input_ids.shape[0] else: prev_k = k if input_ids[k, -1] == self.timestamp_begin: self.no_time_stamp_counter[prev_k] = 0 can_produce = self.no_time_stamp_counter[prev_k] > self.min_space_between_timestamps must_produce = ( input_ids[k][2:].le(self.timestamp_begin).all() and input_ids.shape[-1] == self.max_length - 1 ) # produce timestamp with 30% if (can_produce and self.let_pass[prev_k][self.count]) or must_produce: self.no_time_stamp_counter[prev_k] = 0 self.prev_highest_timestamp[prev_k] = max(input_ids[k].max() + 1, self.timestamp_tokens[0].item()) # force a timestamp scores[k, :] = -float("inf") scores[k, self.prev_highest_timestamp[prev_k]] = 10.0 if ( input_ids.shape[-1] > 3 and input_ids[k, -1].item() in self.timestamp_tokens and input_ids[k, -2].item() not in self.timestamp_tokens ): # force the same as before scores[k, :] = -float("inf") scores[k, input_ids[k, -1].item()] = 10.0 self.count += 1 if torch.isinf(scores).all(): raise ValueError("Dummy logit processor is incorrectly set up. Scores should not be all inf.") return scores if is_torchaudio_available(): import torchaudio def prepare_whisper_inputs_dict( config, input_features, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, ): if decoder_attention_mask is None: decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id) return { # "input_ids": input_features, "input_features": input_features, "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": decoder_attention_mask, } @require_torch class WhisperModelTester: def __init__( self, parent, batch_size=3, # need batch_size != num_hidden_layers seq_length=60, is_training=True, use_labels=False, vocab_size=200, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, input_channels=1, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, max_source_positions=30, max_target_positions=40, bos_token_id=98, eos_token_id=98, pad_token_id=0, num_mel_bins=80, decoder_start_token_id=85, num_conv_layers=1, suppress_tokens=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.input_channels = input_channels self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.num_mel_bins = num_mel_bins self.max_position_embeddings = max_position_embeddings self.max_source_positions = max_source_positions self.max_target_positions = max_target_positions self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.decoder_start_token_id = decoder_start_token_id self.num_conv_layers = num_conv_layers self.suppress_tokens = suppress_tokens def prepare_config_and_inputs(self): input_features = floats_tensor([self.batch_size, self.num_mel_bins, self.seq_length], self.vocab_size) decoder_input_ids = torch.tensor(self.batch_size * [[self.decoder_start_token_id]], device=torch_device) config = self.get_config() inputs_dict = prepare_whisper_inputs_dict( config, attention_mask=None, input_features=input_features, decoder_input_ids=decoder_input_ids, ) return config, inputs_dict def get_config(self): return WhisperConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, input_channels=self.input_channels, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, max_source_positions=self.max_source_positions, max_target_positions=self.max_target_positions, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, decoder_ffn_dim=self.hidden_size, encoder_ffn_dim=self.hidden_size, decoder_start_token_id=self.decoder_start_token_id, suppress_tokens=self.suppress_tokens, ) def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def get_subsampled_output_lengths(self, input_lengths): """ Computes the output length of the convolutional layers """ for i in range(self.num_conv_layers): input_lengths = (input_lengths - 1) // 2 + 1 return input_lengths def create_and_check_model_forward(self, config, inputs_dict, freeze_encoder=False): model = WhisperModel(config=config).to(torch_device).eval() if freeze_encoder: model.freeze_encoder() input_features = inputs_dict["input_features"] decoder_input_ids = inputs_dict["decoder_input_ids"] # first forward pass last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state self.parent.assertTrue(last_hidden_state.shape, (13, 7, 16)) def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = WhisperModel(config=config).get_decoder().to(torch_device).eval() input_ids = inputs_dict["decoder_input_ids"] attention_mask = inputs_dict["decoder_attention_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size).clamp(2) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "last_hidden_state" ] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2)) def check_encoder_decoder_model_standalone(self, config, inputs_dict): model = WhisperModel(config=config).to(torch_device).eval() outputs = model(**inputs_dict) encoder_last_hidden_state = outputs.encoder_last_hidden_state last_hidden_state = outputs.last_hidden_state with tempfile.TemporaryDirectory() as tmpdirname: encoder = model.get_encoder() encoder.save_pretrained(tmpdirname) encoder = WhisperEncoder.from_pretrained(tmpdirname).to(torch_device) encoder_last_hidden_state_2 = encoder(inputs_dict["input_features"])[0] self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3) with tempfile.TemporaryDirectory() as tmpdirname: decoder = model.get_decoder() decoder.save_pretrained(tmpdirname) decoder = WhisperDecoder.from_pretrained(tmpdirname).to(torch_device) last_hidden_state_2 = decoder( input_ids=inputs_dict["decoder_input_ids"], attention_mask=inputs_dict["decoder_attention_mask"], encoder_hidden_states=encoder_last_hidden_state, )[0] self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3) @require_torch class WhisperModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (WhisperModel, WhisperForConditionalGeneration) if is_torch_available() else () pipeline_model_mapping = ( { "audio-classification": WhisperForAudioClassification, "automatic-speech-recognition": WhisperForConditionalGeneration, "feature-extraction": WhisperModel, "text-generation": WhisperForCausalLM, } if is_torch_available() else {} ) is_encoder_decoder = True test_missing_keys = False # Needs higher percentages after model tester's vocab_size is changed to 200 (PR #21222) # `0.5` is for `test_disk_offload` (which also works for `test_model_parallelism`) model_split_percents = [0.5, 0.8, 0.9] # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_case_name, config_class, model_architecture, tokenizer_name, image_processor_name, feature_extractor_name, processor_name, ): if pipeline_test_case_name in [ "AutomaticSpeechRecognitionPipelineTests", "AudioClassificationPipelineTests", ]: # RuntimeError: The size of tensor a (1500) must match the size of tensor b (30) at non-singleton # dimension 1 return True return False def _get_logits_processor_kwargs(self, do_sample=False, config=None): # Overwritten from `GenerationTesterMixin`, Whisper needs `"temperature": 0.0` to be able to do beam search logits_processor_kwargs = super()._get_logits_processor_kwargs(do_sample=do_sample, config=config) logits_processor_kwargs["temperature"] = 0.0 return logits_processor_kwargs def _get_beam_kwargs(self, num_return_sequences=1): # Overwritten from `GenerationTesterMixin`, Whisper's `num_return_sequences` differs from the core `generate` beam_kwargs = super()._get_beam_kwargs(num_return_sequences=num_return_sequences) beam_kwargs["num_return_sequences"] = beam_kwargs["num_beams"] return beam_kwargs def setUp(self): self.model_tester = WhisperModelTester(self) self.config_tester = ConfigTester(self, config_class=WhisperConfig) self.maxDiff = 3000 def prepare_config_and_inputs_for_generate(self, batch_size=2): config, inputs_dict = super().prepare_config_and_inputs_for_generate(batch_size=batch_size) inputs_dict["force_unique_generate_call"] = True return config, inputs_dict def test_config(self): self.config_tester.run_common_tests() def test_save_load_strict(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], set()) def test_model_forward(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_forward(*config_and_inputs) def test_model_forward_with_frozen_encoder(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_forward(*config_and_inputs, freeze_encoder=True) def test_requires_grad_with_frozen_encoder(self): config = self.model_tester.get_config() for model_class in self.all_model_classes: model = model_class(config) model.freeze_encoder() try: encoder_grads = [param.requires_grad for param in model.encoder.parameters()] decoder_grads = [param.requires_grad for param in model.decoder.parameters()] except AttributeError: encoder_grads = [param.requires_grad for param in model.model.encoder.parameters()] decoder_grads = [param.requires_grad for param in model.model.decoder.parameters()] self.assertFalse(all(encoder_grads)) self.assertTrue(all(decoder_grads)) def test_requires_grad_encoder_embed_positions(self): config = self.model_tester.get_config() for model_class in self.all_model_classes: model = model_class(config) encoder = model.get_encoder() self.assertFalse(encoder.embed_positions.weight.requires_grad) def test_encoder_sinusoidal_embed_positions(self): config = self.model_tester.get_config() for model_class in self.all_model_classes: model = model_class(config) embeds = model.get_encoder().embed_positions.weight torch.testing.assert_close(embeds, sinusoids(*embeds.shape)) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_encoder_decoder_model_standalone(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs) def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) decoder_input_ids = inputs.pop("decoder_input_ids", None) inputs.pop("decoder_attention_mask", None) wte = model.get_input_embeddings() inputs["decoder_inputs_embeds"] = wte(decoder_input_ids) with torch.no_grad(): model(**inputs)[0] def test_beam_search_output(self): config, input_dict = self.model_tester.prepare_config_and_inputs() model = WhisperForConditionalGeneration(config).to(torch_device).eval() input_features = input_dict["input_features"] # Perform beam search output = model.generate( input_features, num_beams=3, num_return_sequences=3, return_dict_in_generate=True, output_scores=True ) # Check if beam_indices and sequences_scores are in the output self.assertIn("beam_indices", output, "beam_indices not found in the output") self.assertIn("sequences_scores", output, "sequences_scores not found in the output") # Validate the shapes of the beam_indices and sequences_scores self.assertEqual(output.beam_indices.shape[0], input_features.shape[0] * 3) self.assertEqual(output.sequences_scores.shape[0], input_features.shape[0] * 3) # training is not supported yet @unittest.skip(reason="Training is not supported yet") def test_training(self): pass @unittest.skip(reason="Training is not supported yet") def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @parameterized.expand([("offloaded",)]) @pytest.mark.generate @unittest.skip(reason="Whisper doesn't work with offloaded cache implementation yet") def test_offloaded_cache_implementation(self, cache_implementation): pass @require_torch_fp16 def test_generate_fp16(self): config, input_dict = self.model_tester.prepare_config_and_inputs() config.max_target_positions = 400 input_features = input_dict["input_features"] model = WhisperForConditionalGeneration(config).eval().to(torch_device) input_features = input_features.half() model.half() model.generate(input_features) model.generate(input_features, num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3) def test_generate_language(self): config, input_dict = self.model_tester.prepare_config_and_inputs() input_features = input_dict["input_features"] model = WhisperForConditionalGeneration(config).to(torch_device) # Hack to keep the test fast and not require downloading a model with a generation_config model.generation_config.__setattr__("lang_to_id", {"<|en|>": 1}) model.generation_config.__setattr__("task_to_id", {"transcribe": 2}) # test language code model.generate(input_features, language="en") # test language token model.generate(input_features, language="<|en|>") # test language name model.generate(input_features, language="English") # test language code list model.generate(input_features, language=["en"] * input_features.shape[0]) # test language token list model.generate(input_features, language=["<|en|>"] * input_features.shape[0]) # test language name list model.generate(input_features, language=["English"] * input_features.shape[0]) # test list of the wrong length with self.assertRaises(ValueError): model.generate(input_features, language=["en"] * (input_features.shape[0] + 1)) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = [ "input_features", "attention_mask", "decoder_input_ids", "decoder_attention_mask", ] expected_arg_names.extend(["encoder_outputs"]) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) if hasattr(self.model_tester, "encoder_seq_length"): seq_length = self.model_tester.encoder_seq_length else: seq_length = self.model_tester.seq_length subsampled_seq_length = model._get_feat_extract_output_lengths(seq_length) self.assertListEqual( list(hidden_states[0].shape[-2:]), [subsampled_seq_length, self.model_tester.hidden_size], ) if config.is_encoder_decoder: hidden_states = outputs.decoder_hidden_states self.assertIsInstance(hidden_states, (list, tuple)) self.assertEqual(len(hidden_states), expected_num_layers) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", 1) self.assertListEqual( list(hidden_states[0].shape[-2:]), [decoder_seq_length, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True # force eager attention to support output attentions config._attn_implementation = "eager" seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", 1) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) decoder_key_length = getattr(self.model_tester, "decoder_key_length", 1) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class._from_config(config, attn_implementation="eager") config = model.config model.to(torch_device) model.eval() subsampled_encoder_seq_length = model._get_feat_extract_output_lengths(encoder_seq_length) subsampled_encoder_key_length = model._get_feat_extract_output_lengths(encoder_key_length) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length], ) out_len = len(outputs) correct_outlen = 5 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning if "past_key_values" in outputs: correct_outlen += 1 # past_key_values have been returned self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions self.assertIsInstance(decoder_attentions, (list, tuple)) self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length], ) # cross attentions cross_attentions = outputs.cross_attentions self.assertIsInstance(cross_attentions, (list, tuple)) self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, decoder_seq_length, subsampled_encoder_key_length, ], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) added_hidden_states = 2 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length], ) def test_resize_tokens_embeddings(self): ( original_config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: self.skipTest(reason="test_resize_embeddings is False") for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) if self.model_tester.is_training is False: model.eval() model_vocab_size = config.vocab_size # Retrieve the embeddings and clone theme model_embed = model.resize_token_embeddings(model_vocab_size) cloned_embeddings = model_embed.weight.clone() # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.vocab_size, model_vocab_size + 10) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15) # make sure that decoder_input_ids are resized if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that adding and removing tokens has not modified the first part of the embedding matrix. models_equal = True for p1, p2 in zip(cloned_embeddings, model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_resize_embeddings_untied(self): ( original_config, inputs_dict, ) = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: self.skipTest(reason="test_resize_embeddings is False") original_config.tie_word_embeddings = False # if model cannot untied embeddings -> leave test if original_config.tie_word_embeddings: self.skipTest(reason="Model cannot untie embeddings") for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config).to(torch_device) model.eval() # if no output embeddings -> leave test if model.get_output_embeddings() is None: continue # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_vocab_size = config.vocab_size model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.vocab_size, model_vocab_size + 10) output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) @unittest.skip(reason="Whisper encoder-decoder requires the features directly and can not work on ids only.") def test_generate_without_input_ids(self): pass def test_mask_feature_prob(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.mask_feature_prob = 0.2 config.mask_feature_length = 2 for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.train() # forward pass encoder_last_hidden_state = model(**input_dict).encoder_last_hidden_state self.assertTrue(encoder_last_hidden_state.shape, (13, 30, 16)) def test_mask_time_prob(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.mask_time_prob = 0.2 config.mask_time_length = 2 for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.train() # forward pass encoder_last_hidden_state = model(**input_dict).encoder_last_hidden_state self.assertTrue(encoder_last_hidden_state.shape, (13, 30, 16)) def test_generate_with_prompt_ids_max_length(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.max_target_positions = 7 model = WhisperForConditionalGeneration(config).eval().to(torch_device) input_features = input_dict["input_features"] decoder_input_ids = torch.arange(5).to(torch_device) prompt_ids = decoder_input_ids[:4] max_new_tokens = 8 with self.assertRaisesRegex( ValueError, f"The length of `decoder_input_ids`, including special start tokens, prompt tokens, and previous tokens, is {decoder_input_ids.shape[-1]}, " f" and `max_new_tokens` is {max_new_tokens}. Thus, the combined length of " f"`decoder_input_ids` and `max_new_tokens` is: {max_new_tokens + decoder_input_ids.shape[-1]}. This exceeds the " f"`max_target_positions` of the Whisper model: {config.max_target_positions}. " "You should either reduce the length of your prompt, or reduce the value of `max_new_tokens`, " f"so that their combined length is less than {config.max_target_positions}.", ): model.generate(input_features, max_new_tokens=max_new_tokens, prompt_ids=prompt_ids) model.generate(input_features, max_new_tokens=1, prompt_ids=prompt_ids) def test_generate_longform_with_prompt_ids(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() model = WhisperForConditionalGeneration(config).eval().to(torch_device) prompt_ids = torch.arange(5).to(torch_device) model.generation_config.no_timestamps_token_id = 11 model.generation_config.pad_token_id = 10 # make sure prompt token ids [0-9] can't be generated model.generation_config.suppress_tokens = list(range(10)) input_features = input_dict["input_features"] language = "<|de|>" lang_id = 6 input_features = input_features.repeat(1, 1, 50) attention_mask = torch.ones_like(input_features, dtype=torch.long)[:, 0] for prompt_type in ["first-segment", "all-segments"]: for task_id, task in enumerate(["translate", "transcribe"]): task_id = 7 + task_id model.generation_config.__setattr__("lang_to_id", {language: lang_id}) model.generation_config.__setattr__("task_to_id", {task: task_id}) output = model.generate( input_features, attention_mask=attention_mask, prompt_condition_type=prompt_type, max_new_tokens=5, task=task, language=language, prompt_ids=prompt_ids, condition_on_prev_tokens=True, ) for row in output.tolist(): # make sure no token below 10 is in generated output => this means for long-form prompt ids should NOT be returned self.assertTrue(not any(i in row for i in model.generation_config.suppress_tokens)) def _check_longform_generate_single_batch(self, condition_on_prev_tokens): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() model = WhisperForConditionalGeneration(config).eval().to(torch_device) input_features = input_dict["input_features"] # len = 250 with num_input_frames = 60 long_input_features = torch.cat([input_features.repeat(1, 1, 4), input_features[:, :, :10]], dim=-1) # force bsz=1 long_input_features = long_input_features[:1] vocab_size = model.config.vocab_size batch_size = 1 num_timestamp_tokens = 20 max_length = 16 logits_processor = [ DummyTimestampLogitProcessor( vocab_size - num_timestamp_tokens, vocab_size, batch_size=batch_size, max_length=max_length, min_space=4, ) ] # each chunk should not be longer than 10 model.generation_config.max_length = max_length # if input features are long can't set return_timestamps to False with self.assertRaises(ValueError): _ = model.generate(long_input_features, logits_processor=logits_processor, return_timestamps=False) # if input features are long need to set generation config with self.assertRaises(ValueError): _ = model.generate(long_input_features, logits_processor=logits_processor) timestamp_begin = vocab_size - num_timestamp_tokens model.generation_config.no_timestamps_token_id = timestamp_begin - 1 model.generation_config.eos_token_id = None model.config.eos_token_id = None model.generation_config._detect_timestamp_from_logprob = False # make sure that we only have the same begin token model.generation_config.max_initial_timestamp_index = 0 model.generation_config.prev_bos_token_id = timestamp_begin - 3 gen_kwargs = { "logits_processor": logits_processor, "return_segments": True, "condition_on_prev_tokens": condition_on_prev_tokens, } if condition_on_prev_tokens: gen_kwargs["no_speech_threshold"] = 0.6 gen_kwargs["temperature"] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0) gen_kwargs["compression_ratio_threshold"] = 2.4 gen_kwargs["logprob_threshold"] = -1.0 outputs = model.generate(long_input_features, **gen_kwargs) segments = outputs["segments"][0] for _, segment in enumerate(segments): self.assertTrue(segment["start"] <= segment["end"], "start has to be smaller equal end") self.assertTrue( any(s > timestamp_begin for s in segment["tokens"][1:]), f"At least one segment token should be a timestamp token, but not first., {segment['tokens']}", ) self.assertTrue( segment["tokens"].shape[-1] <= max_length, "make sure that no segment is larger than max generation length", ) def test_longform_generate_single_batch(self): self._check_longform_generate_single_batch(condition_on_prev_tokens=False) def test_longform_generate_single_batch_cond_prev(self): self._check_longform_generate_single_batch(condition_on_prev_tokens=True) def _check_longform_generate_multi_batch(self, condition_on_prev_tokens): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() model = WhisperForConditionalGeneration(config).eval().to(torch_device) input_features = input_dict["input_features"].to(torch_device) input_features = input_features[:2] # len = 250 with num_input_frames = 60 long_input_features = torch.cat([input_features.repeat(1, 1, 4), input_features[:, :, :10]], dim=-1) input_features_2 = long_input_features[1:] attention_mask = torch.ones( (2, long_input_features.shape[-1]), dtype=input_features.dtype, device=input_features.device ) attention_mask[0, 200:] = 0 # force bsz=1 vocab_size = model.config.vocab_size batch_size = 1 num_timestamp_tokens = 20 max_new_tokens = 16 timestamp_begin = vocab_size - num_timestamp_tokens model.generation_config.no_timestamps_token_id = timestamp_begin - 1 model.generation_config.eos_token_id = None model.config.eos_token_id = None model.generation_config._detect_timestamp_from_logprob = False # make sure that we only have the same begin token model.generation_config.max_initial_timestamp_index = 0 model.generation_config.max_new_tokens = max_new_tokens model.generation_config.prev_bos_token_id = timestamp_begin - 3 logits_processor = [ DummyTimestampLogitProcessor( vocab_size - num_timestamp_tokens, vocab_size, batch_size=batch_size, max_length=max_new_tokens, min_space=4, seed=1, ) ] outputs_2 = model.generate( input_features_2, max_new_tokens=max_new_tokens, logits_processor=logits_processor, condition_on_prev_tokens=condition_on_prev_tokens, return_segments=True, ) tokens_2 = outputs_2["sequences"][0] segments_2 = outputs_2["segments"][0] batch_size = 2 logits_processor = [ DummyTimestampLogitProcessor( vocab_size - num_timestamp_tokens, vocab_size, batch_size=batch_size, max_length=max_new_tokens, min_space=4, seed=0, ) ] gen_kwargs = { "logits_processor": logits_processor, "return_segments": True, "condition_on_prev_tokens": condition_on_prev_tokens, "attention_mask": attention_mask, "max_new_tokens": max_new_tokens, } outputs = model.generate(long_input_features, **gen_kwargs) tokens = outputs["sequences"][1] segments = outputs["segments"][1] # make sure batched and non-batched is the same self.assertEqual(tokens_2.tolist(), tokens[: tokens_2.shape[-1]].tolist()) for seg1, seg2 in zip(segments_2, segments): self.assertEqual(seg1["start"], seg2["start"]) self.assertEqual(seg1["end"], seg2["end"]) self.assertEqual(seg1["tokens"].tolist(), seg2["tokens"].tolist()) def test_longform_generate_multi_batch(self): self._check_longform_generate_multi_batch(condition_on_prev_tokens=False) def test_longform_generate_multi_batch_cond_prev(self): self._check_longform_generate_multi_batch(condition_on_prev_tokens=True) @is_flaky() # TODO (joao, sanchit): fails ~9% of the times. Does the original test have the same issue? def test_custom_4d_attention_mask(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() model = WhisperForConditionalGeneration(config).to(device=torch_device, dtype=torch.float32) model.eval() ( input_ids, position_ids, input_ids_shared_prefix, mask_shared_prefix, position_ids_shared_prefix, ) = self._get_custom_4d_mask_test_data() with torch.no_grad(): logits = model.forward( decoder_input_ids=input_ids, input_features=input_dict["input_features"], decoder_position_ids=position_ids, ).logits # logits.shape == torch.Size([3, 4, ...]) logits_shared_prefix = model( decoder_input_ids=input_ids_shared_prefix, input_features=input_dict["input_features"], decoder_attention_mask=mask_shared_prefix, decoder_position_ids=position_ids_shared_prefix, )[0] # logits_shared_prefix.shape == torch.Size([1, 6, ...]) out_last_tokens = logits[:, -1, :] # last tokens in each batch line out_shared_prefix_last_tokens = logits_shared_prefix[0, -3:, :] # last three tokens # comparing softmax-normalized logits: normalized_0 = torch.nn.functional.softmax(out_last_tokens) normalized_1 = torch.nn.functional.softmax(out_shared_prefix_last_tokens) torch.testing.assert_close(normalized_0, normalized_1, rtol=1e-3, atol=1e-4) @parameterized.expand([(True,), (False,)]) def test_generate_output_type(self, return_dict_in_generate): expected_output_type = GenerateEncoderDecoderOutput if return_dict_in_generate else torch.Tensor for model_class in self.all_generative_model_classes: config, inputs = self.model_tester.prepare_config_and_inputs() model = model_class(config).to(torch_device).eval() # short-form generation without fallback pred_ids = model.generate(**inputs, return_dict_in_generate=return_dict_in_generate) self.assertIsInstance(pred_ids, expected_output_type) # short-form generation with fallback pred_ids = model.generate( **inputs, logprob_threshold=-1.0, temperature=[0.0, 0.1], return_dict_in_generate=return_dict_in_generate, ) self.assertIsInstance(pred_ids, expected_output_type) def test_labels_sequence_max_length_correct(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_generative_model_classes: input_features = input_dict["input_features"] labels_length = config.max_target_positions labels = torch.ones(1, labels_length, dtype=torch.int64).to(torch_device) model = model_class(config).to(torch_device) model(input_features=input_features, labels=labels) def test_labels_sequence_max_length_correct_after_changing_config(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_generative_model_classes: input_features = input_dict["input_features"] config.max_target_positions += 100 labels_length = config.max_target_positions labels = torch.ones(1, labels_length, dtype=torch.int64).to(torch_device) model = model_class(config).to(torch_device) model(input_features=input_features, labels=labels) def test_labels_sequence_max_length_error(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_generative_model_classes: input_features = input_dict["input_features"] labels_length = config.max_target_positions + 1 labels = torch.ones(1, labels_length, dtype=torch.int64).to(torch_device) model = model_class(config).to(torch_device) with self.assertRaises(ValueError): model(input_features=input_features, labels=labels) def test_labels_sequence_max_length_error_after_changing_config(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_generative_model_classes: model = model_class(config).to(torch_device) input_features = input_dict["input_features"] labels_length = config.max_target_positions + 1 labels = torch.ones(1, labels_length, dtype=torch.int64).to(torch_device) new_max_length = config.max_target_positions + 100 model.config.max_length = new_max_length model.generation_config.max_length = new_max_length config.max_target_positions = new_max_length with self.assertRaises(ValueError): model(input_features=input_features, labels=labels) # TODO (joao, eustache): fix me :) The model is not returning a `Cache` by default @unittest.skip(reason="Whisper's custom generate is not consistent regarding the cache return types") @pytest.mark.torch_compile_test def test_generate_compile_model_forward_fullgraph(self): pass # TODO (joao, eustache): fix me :) @unittest.skip(reason="A CUDA exception is thrown when storing extra outputs") def test_generate_compilation_all_outputs(self): pass @require_torch @require_torchaudio class WhisperModelIntegrationTests(unittest.TestCase): _dataset = None @classmethod def _load_dataset(cls): # Lazy loading of the dataset. Because it is a class method, it will only be loaded once per pytest process. if cls._dataset is None: cls._dataset = datasets.load_dataset( "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation" ) def _load_datasamples(self, num_samples): self._load_dataset() ds = self._dataset speech_samples = ds.sort("id")[:num_samples]["audio"] return [x["array"] for x in speech_samples] @slow def test_tiny_logits_librispeech(self): torch_device = "cpu" set_seed(0) model = WhisperModel.from_pretrained("openai/whisper-tiny") model.to(torch_device) input_speech = self._load_datasamples(1) feature_extractor = WhisperFeatureExtractor() input_features = feature_extractor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features with torch.no_grad(): logits = model( input_features, decoder_input_ids=torch.tensor([[50258, 50259, 50359]]), output_hidden_states=False, output_attentions=False, return_dict=False, use_cache=False, ) # fmt: off EXPECTED_LOGITS = torch.tensor( [ 2.9892, -6.7607, 5.7348, 3.6096, 0.2152, -5.7321, 4.8855, -1.6407, 0.2823, -1.5718, 10.4269, 3.4427, 0.0219, -8.0612, 3.4784, 8.4246, 4.0575, -2.2864, 11.1084, 0.9963, 0.9884, -8.5154, -3.5469, -9.3713, 0.9786, 3.5435, 7.4850, -5.2579, -1.4366, 10.4841 ] ) # fmt: on torch.testing.assert_close(logits[0][0, 0, :30].cpu(), EXPECTED_LOGITS, rtol=1e-4, atol=1e-4) # fmt: off EXPECTED_GENERATION = torch.tensor( [ -1.4651, -2.6944, 2.7821, 2.3793, 4.0738, 0.0188, -3.3203, 1.9836, 0.0520, 0.7095, 1.1063, 0.2952, -3.6786, -0.5249, 0.3105, 4.7691, 1.1562, 1.3046, 0.5810, -0.3624, 1.7006, 1.3424, 0.9817, 2.1958, 1.8775, -5.7046, -0.7679, 4.0113, 2.6848, 2.8609 ] ) # fmt: on head_logits = logits[0] @ model.decoder.embed_tokens.weight.T torch.testing.assert_close(head_logits[0, 0, :30].cpu(), EXPECTED_GENERATION, rtol=1e-4, atol=1e-4) @slow def test_small_en_logits_librispeech(self): set_seed(0) torch_device = "cpu" model = WhisperModel.from_pretrained("openai/whisper-small.en") model.to(torch_device) input_speech = self._load_datasamples(1) feature_extractor = WhisperFeatureExtractor() input_features = feature_extractor(input_speech, return_tensors="pt").input_features.to(torch_device) logits = model( input_features, decoder_input_ids=torch.tensor([[model.config.decoder_start_token_id]]), output_hidden_states=False, output_attentions=False, use_cache=False, ) logits = logits.last_hidden_state @ model.decoder.embed_tokens.weight.T # fmt: off EXPECTED_LOGITS = torch.tensor( [ -3.6784, -7.7211, -9.5070, -11.9286, -7.6489, -9.7026, -5.6188, -8.0104, -4.6238, -5.1833, -9.0485, -3.4079, -5.4874, -2.6935, -6.3479, -7.3398, -6.9558, -7.6867, -7.4748, -8.3463, -9.9781, -10.8389, -10.3105, -11.7201, -9.7261, -7.1590, -5.9272, -12.4509, -11.1146, -8.1918 ] ) # fmt: on torch.testing.assert_close(logits[0, 0, :30].cpu(), EXPECTED_LOGITS, rtol=1e-4, atol=1e-4) @slow def test_large_logits_librispeech(self): set_seed(0) torch_device = "cpu" model = WhisperModel.from_pretrained("openai/whisper-large") model.to(torch_device) input_speech = self._load_datasamples(1) processor = WhisperProcessor.from_pretrained("openai/whisper-large") processed_inputs = processor( audio=input_speech, text="This part of the speech", add_special_tokens=False, return_tensors="pt", sampling_rate=16_000, ) input_features = processed_inputs.input_features.to(torch_device) decoder_input_ids = processed_inputs.labels.to(torch_device) logits = model( input_features, decoder_input_ids=decoder_input_ids, output_hidden_states=False, output_attentions=False, use_cache=False, ) logits = logits.last_hidden_state @ model.decoder.embed_tokens.weight.T # fmt: off EXPECTED_LOGITS = torch.tensor( [ 2.1382, 0.9381, 4.4671, 3.5589, 2.4022, 3.8576, -0.6521, 2.5472, 1.8301, 1.9957, 2.3432, 1.4678, 0.5459, 2.2597, 1.5179, 2.5357, 1.1624, 0.6194, 1.0757, 1.8259, 2.4076, 1.6601, 2.3503, 1.3376, 1.9891, 1.8635, 3.8931, 5.3699, 4.4772, 3.9184 ] ) # fmt: on torch.testing.assert_close(logits[0, 0, :30].cpu(), EXPECTED_LOGITS, rtol=1e-4, atol=1e-4) @slow def test_tiny_en_generation(self): processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") model.to(torch_device) model.config.decoder_start_token_id = 50257 input_speech = self._load_datasamples(1) input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features input_features = input_features.to(torch_device) generated_ids = model.generate(input_features, num_beams=5, max_length=20) transcript = processor.tokenizer.batch_decode(generated_ids)[0] EXPECTED_TRANSCRIPT = " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his" self.assertEqual(transcript, EXPECTED_TRANSCRIPT) @slow def test_tiny_generation(self): processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") model.to(torch_device) input_speech = self._load_datasamples(1) input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features input_features = input_features.to(torch_device) generated_ids = model.generate(input_features, num_beams=5, max_length=20) transcript = processor.tokenizer.decode(generated_ids[0]) EXPECTED_TRANSCRIPT = " Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel" self.assertEqual(transcript, EXPECTED_TRANSCRIPT) @slow def test_large_generation(self): processor = WhisperProcessor.from_pretrained("openai/whisper-large-v3") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3") model.to(torch_device) input_speech = self._load_datasamples(1) input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features input_features = input_features.to(torch_device) generated_ids = model.generate( input_features, do_sample=False, max_length=20, language="<|en|>", task="transcribe" ) transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] EXPECTED_TRANSCRIPT = " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his" self.assertEqual(transcript, EXPECTED_TRANSCRIPT) @slow def test_large_generation_multilingual(self): processor = WhisperProcessor.from_pretrained("openai/whisper-large-v3") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3") model.to(torch_device) ds = load_dataset("facebook/multilingual_librispeech", "german", split="test", streaming=True) ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000)) input_speech = next(iter(ds))["audio"]["array"] input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features input_features = input_features.to(torch_device) generated_ids = model.generate( input_features, do_sample=False, max_length=20, language="<|de|>", task="transcribe" ) transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] EXPECTED_TRANSCRIPT = " denken sie soeben weilten meine gedanken bei ihnen in adelaide und ich wünsch" self.assertEqual(transcript, EXPECTED_TRANSCRIPT) generated_ids = model.generate( input_features, do_sample=False, max_length=20, language="<|de|>", task="translate" ) transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] EXPECTED_TRANSCRIPT = " Think, my thoughts were just now in Adelaide with you, and I wished to be able" self.assertEqual(transcript, EXPECTED_TRANSCRIPT) @slow def test_large_batched_generation(self): set_seed(0) processor = WhisperProcessor.from_pretrained("openai/whisper-large-v3") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3") model.to(torch_device) input_speech = self._load_datasamples(4) input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features input_features = input_features.to(torch_device) generated_ids = model.generate(input_features, max_length=20, task="translate") # fmt: off EXPECTED_LOGITS = torch.tensor( [ [2221, 13, 2326, 388, 391, 307, 264, 50244, 295, 264, 2808, 5359, 293, 321, 366, 5404, 281, 2928, 702, 14943], [6966, 307, 2221, 13, 2326, 388, 391, 311, 9060, 1570, 1880, 813, 702, 1871, 13, 50257, 50257, 50257, 50257, 50257], [415, 5112, 505, 300, 412, 341, 42729, 3196, 295, 264, 1064, 365, 26586, 3799, 293, 12904, 9256, 450, 10539, 949], [634, 575, 12525, 22618, 1968, 6144, 35617, 1456, 397, 266, 311, 589, 307, 534, 10281, 934, 439, 11, 293, 393] ] ) # fmt: on torch.testing.assert_close(generated_ids.cpu(), EXPECTED_LOGITS) # fmt: off EXPECTED_TRANSCRIPT = [ " Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel", " Nor is Mr. Quilter's manner less interesting than his matter.", " he tells us that at this festive season of the year with christmas and roast beef looming before", " He has grave doubts whether Sir Frederick Leighton's work is really Greek after all, and can", ] # fmt: on transcript = processor.batch_decode(generated_ids, skip_special_tokens=True) self.assertListEqual(transcript, EXPECTED_TRANSCRIPT) @require_read_token @slow def test_large_batched_generation_multilingual(self): processor = WhisperProcessor.from_pretrained("openai/whisper-large") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large") model.to(torch_device) token = os.getenv("HF_HUB_READ_TOKEN", None) if token is None: token = True ds = load_dataset( "hf-internal-testing/fixtures_common_voice", "ja", split="test", streaming=True, token=token, ) ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000)) input_speech = next(iter(ds))["audio"]["array"] input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="pt").input_features.to( torch_device ) EXPECTED_TRANSCRIPTS = [ "夏の時期の時期でした", " It was the time of day and all of the pens left during the summer.", ] generated_ids = model.generate( input_features.repeat(2, 1, 1), do_sample=False, max_length=20, language=["<|ja|>", "<|en|>"], task="transcribe", ) transcripts = processor.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(transcripts, EXPECTED_TRANSCRIPTS) @slow def test_tiny_en_batched_generation(self): set_seed(0) processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") model.to(torch_device) input_speech = self._load_datasamples(4) input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features input_features = input_features.to(torch_device) generated_ids = model.generate(input_features, max_length=20).to("cpu") # fmt: off EXPECTED_LOGITS = torch.tensor( [ [1770, 13, 2264, 346, 353, 318, 262, 46329, 286, 262, 3504, 6097, 11, 290, 356, 389, 9675, 284, 7062, 465], [5414, 318, 1770, 13, 2264, 346, 353, 338, 5642, 1342, 3499, 621, 465, 2300, 13, 50256, 50256, 50256, 50256, 50256], [679, 4952, 514, 326, 379, 428, 43856, 1622, 286, 262, 614, 11, 351, 6786, 290, 32595, 12023, 28236, 878, 514], [679, 468, 12296, 17188, 1771, 7361, 26113, 18881, 1122, 338, 670, 318, 1107, 8312, 706, 477, 290, 460, 7073, 287] ] ) # fmt: on torch.testing.assert_close(generated_ids, EXPECTED_LOGITS) # fmt: off EXPECTED_TRANSCRIPT = [ " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his", " Nor is Mr. Quilter's manner less interesting than his matter.", " He tells us that at this festive season of the year, with Christmas and roast beef looming before us", " He has grave doubts whether Sir Frederick Layton's work is really Greek after all and can discover in", ] # fmt: on transcript = processor.batch_decode(generated_ids, skip_special_tokens=True) self.assertListEqual(transcript, EXPECTED_TRANSCRIPT) @slow def test_tiny_timestamp_generation(self): set_seed(0) processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") model.to(torch_device) input_speech = np.concatenate(self._load_datasamples(4)) input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features input_features = input_features.to(torch_device) generated_ids = model.generate(input_features, max_length=448, return_timestamps=True).to("cpu") # fmt: off EXPECTED_OUTPUT = torch.tensor([ [50364, 2221, 13, 2326, 388, 391, 307, 264, 50244, 295, 264, 2808, 5359, 11, 293, 321, 366, 5404, 281, 2928, 702, 14943, 13, 50692, 50692, 6966, 307, 2221, 13, 2326, 388, 391, 311, 9060, 1570, 1880, 813, 702, 1871, 13, 50926, 50926, 634, 5112, 505, 300, 412, 341, 42729, 3196, 295, 264, 1064, 11, 365, 5272, 293, 12904, 9256, 450, 10539, 51208, 51208, 949, 505, 11, 14138, 10117, 490, 3936, 293, 1080, 3542, 5160, 881, 26336, 281, 264, 1575, 13, 51552, 51552, 634, 575, 12525, 22618, 1968, 6144, 35617, 7354, 1292, 6, 589, 307, 534, 10281, 934, 439, 11, 293, 51836, 51836, 50364, 393, 4411, 13, 50514] ]) # fmt: on torch.testing.assert_close(generated_ids, EXPECTED_OUTPUT) EXPECTED_TRANSCRIPT = [ { "text": ( " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel. Nor is" " Mr. Quilter's manner less interesting than his matter. He tells us that at this festive season" " of the year, with Christmas and roast beef looming before us, similarly drawn from eating and" " its results occur most readily to the mind. He has grave doubts whether Sir Frederick Latins'" " work is really Greek after all, and can discover." ), "offsets": [ { "text": ( " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel." ), "timestamp": (0.0, 6.5600000000000005), }, { "text": " Nor is Mr. Quilter's manner less interesting than his matter.", "timestamp": (6.5600000000000005, 11.24), }, { "text": ( " He tells us that at this festive season of the year, with Christmas and roast beef" " looming" ), "timestamp": (11.24, 16.88), }, { "text": ( " before us, similarly drawn from eating and its results occur most readily to the mind." ), "timestamp": (16.88, 23.76), }, { "text": ( " He has grave doubts whether Sir Frederick Latins' work is really Greek after all, and" ), "timestamp": (23.76, 29.44), }, { "text": " can discover.", "timestamp": (29.44, 32.44), }, ], } ] transcript = processor.batch_decode(generated_ids, skip_special_tokens=True, output_offsets=True) self.assertEqual(transcript, EXPECTED_TRANSCRIPT) @slow def test_distil_token_timestamp_generation(self): # we actually just want to check that returning segments with distil model works processor = WhisperProcessor.from_pretrained("distil-whisper/distil-large-v3") model = WhisperForConditionalGeneration.from_pretrained("distil-whisper/distil-large-v3") model.to(torch_device) input_speech = np.concatenate(self._load_datasamples(4)) input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features input_features = input_features.to(torch_device) _ = model.generate( input_features, max_length=448, return_timestamps=True, return_token_timestamps=True, return_segments=True ) @slow def test_tiny_longform_timestamps_generation(self): set_seed(0) processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") model.to(torch_device) dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation") sample = dataset[0]["audio"] input_features = processor( sample["array"], return_tensors="pt", truncation=False, sampling_rate=sample["sampling_rate"] ) input_features = input_features.to(torch_device) generated_ids = model.generate(**input_features, return_timestamps=True, return_segments=True) EXPECTED_TRANSCRIPT = [ { "text": " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.", "timestamp": (0.0, 6.5600000000000005), }, { "text": " Nor is Mr. Quilter's manner less interesting than his matter.", "timestamp": (6.5600000000000005, 11.24), }, { "text": " He tells us that at this festive season of the year, with Christmas and roast beef looming", "timestamp": (11.24, 16.88), }, { "text": " before us, similarly drawn from eating and its results occur most readily to the mind.", "timestamp": (16.88, 23.76), }, { "text": " He has grave doubts whether Sir Frederick Latins' work is really Greek after all, and", "timestamp": (23.76, 29.44), }, {"text": " can discover in it but little of rocky ithaka.", "timestamp": (29.44, 33.72)}, { "text": " Lennils, pictures, are a sort of upguards and atom paintings, and Mason's exquisite itals", "timestamp": (33.72, 40.32), }, {"text": " are as national as a jingo poem.", "timestamp": (40.32, 44.72)}, { "text": " Mr. Birkut Foster's landscapes smile at one much in the same way that Mr. Carker used", "timestamp": (44.72, 50.400000000000006), }, {"text": " to flash his teeth.", "timestamp": (50.400000000000006, 52.96)}, { "text": " And Mr. John Collier gives his sitter a cheerful slap on the back before he says, like", "timestamp": (52.96, 58.68000000000001), }, {"text": " a shampoo and a Turkish bath next man.", "timestamp": (58.68, 61.96)}, ] transcript = processor.batch_decode(generated_ids["sequences"], skip_special_tokens=True, output_offsets=True) self.assertEqual(transcript[0]["offsets"], EXPECTED_TRANSCRIPT) @slow def test_small_longform_timestamps_generation(self): processor = WhisperProcessor.from_pretrained("openai/whisper-small.en") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small.en") model.to(torch_device) dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation") sample = dataset[0]["audio"]["array"] sampling_rate = dataset[0]["audio"]["sampling_rate"] sample = [*sample[: 15 * sampling_rate], *np.zeros(16 * sampling_rate).tolist(), *sample[15 * sampling_rate :]] sample = np.array(sample) input_features = processor( sample, sampling_rate=16_000, padding="longest", truncation=False, return_attention_mask=True, return_tensors="pt", ).input_features input_features = input_features.to(torch_device) generated_ids = model.generate(input_features, return_timestamps=True, return_segments=True) # fmt: off EXPECTED_XPU = [ { 'text': ' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.', 'timestamp': (0.0, 6.38), }, { 'text': " Nor is Mr. Quilter's manner less interesting than his matter.", 'timestamp': (6.38, 11.32), }, { 'text': ' He tells us that at this festive season of the year,', 'timestamp': (11.32, 15.0), }, { 'text': ' With Christmas and roast beef looming before us, similes drawn from eating and its results', 'timestamp': (30.0, 36.76), }, { 'text': ' occur most readily to the mind.', 'timestamp': (36.76, 39.8), }, { 'text': " He has grave doubts whether Sir Frederick Layton's work is really Greek after all and", 'timestamp': (39.8, 45.38), }, { 'text': ' can discover in it but little of rocky Ithaca.', 'timestamp': (45.38, 49.0), }, { 'text': " Lenell's pictures are a sort of up-guards-and-atom paintings, and Mason's exquisite ittles", 'timestamp': (49.0, 56.28), }, { 'text': " are as national as a jingo poem. Mr. Burkett fosters landscape's smile at one much in", 'timestamp': (56.28, 64.12), }, { 'text': ' the same way that Mr. Karker used to flash his teeth. And Mr. John Collier gives his', 'timestamp': (64.12, 70.76), }, { 'text': ' sitter a cheerful slap on the back before he says, like a shampoo or in a Turkish bath,', 'timestamp': (70.76, 77.16), }, { 'text': ' Next Man', 'timestamp': (77.16, 78.16), }, ] EXPECTED_CUDA = [ { "text": " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.", "timestamp": (0.0, 6.38), }, { "text": " Nor is Mr. Quilter's manner less interesting than his matter.", "timestamp": (6.38, 11.32), }, { "text": " He tells us that at this festive season of the year,", "timestamp": (11.32, 15.0), }, { "text": " With Christmas and roast beef looming before us, similes drawn from eating and its results", "timestamp": (30.0, 36.76), }, { "text": " occur most readily to the mind.", "timestamp": (36.76, 39.80), }, { "text": " He has grave doubts whether Sir Frederick Layton's work is really Greek after all and", "timestamp": (39.80, 45.36), }, { "text": " can discover in it but little of rocky Ithaca.", "timestamp": (45.36, 49.0), }, { "text": " Lenell's pictures are a sort of up-guards-and-atom paintings, and Mason's exquisite ittles", "timestamp": (49.0, 56.28), }, { "text": " are as national as a jingo poem. Mr. Burkett fosters landscape's smile at one much in", "timestamp": (56.28, 64.12), }, { "text": " the same way that Mr. Karker used to flash his teeth. And Mr. John Collier gives his", "timestamp": (64.12, 70.76), }, { "text": " sitter a cheerful slap on the back before he says, like a shampoo or in a Turkish bath,", "timestamp": (70.76, 77.16), }, { "text": " Next Man", "timestamp": (77.16, 78.16), }, ] EXPECTED_ROCM = [ { "text": " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.", "timestamp": (0.0, 6.38), }, { "text": " Nor is Mr. Quilter's manner less interesting than his matter.", "timestamp": (6.38, 11.32), }, { "text": " He tells us that at this festive season of the year,", "timestamp": (11.32, 15.0), }, { "text": " With Christmas and roast beef looming before us, similes drawn from eating and its results", "timestamp": (30.0, 36.76), }, { "text": " occur most readily to the mind.", "timestamp": (36.76, 39.8), }, { "text": " He has grave doubts whether Sir Frederick Layton's work is really Greek after all and", "timestamp": (39.8, 45.38), }, { "text": " can discover in it but little of rocky Ithaca.", "timestamp": (45.38, 49.0), }, { "text": " Lenell's pictures are a sort of up-guards-and-atom paintings, and Mason's exquisite ittles", "timestamp": (49.0, 56.28), }, { "text": " are as national as a jingo poem. Mr. Burkett fosters landscape's smile at one much in", "timestamp": (56.28, 64.12), }, { "text": " the same way that Mr. Karker used to flash his teeth. And Mr. John Collier gives his", "timestamp": (64.12, 70.76), }, { "text": " sitter a cheerful slap on the back before he says, like a shampoo or in a Turkish bath,", "timestamp": (70.76, 77.16), }, { "text": " Next Man", "timestamp": (77.16, 78.16), }, ] # fmt: on expected_output = Expectations( {("xpu", None): EXPECTED_XPU, ("cuda", None): EXPECTED_CUDA, ("rocm", (9, 4)): EXPECTED_ROCM} ).get_expectation() transcript = processor.batch_decode(generated_ids["sequences"], skip_special_tokens=True, output_offsets=True) self.assertEqual(transcript[0]["offsets"], expected_output) transcript_segments = [ { "text": processor.decode(seg["tokens"], skip_special_tokens=True), "timestamp": (seg["start"].item(), seg["end"].item()), } for seg in generated_ids["segments"][0] ] self.assertEqual(transcript_segments, expected_output) @slow def test_large_timestamp_generation(self): set_seed(0) processor = WhisperProcessor.from_pretrained("openai/whisper-large-v3") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3") model.to(torch_device) input_speech = np.concatenate(self._load_datasamples(4)) input_features = processor( input_speech, return_tensors="pt", sampling_rate=16_000, ).input_features input_features = input_features.to(torch_device) generated_ids = model.generate( input_features, max_length=448, return_timestamps=True, condition_on_prev_tokens=True ).to("cpu") # fmt: off EXPECTED_OUTPUT = torch.tensor([ [50365, 2221, 13, 2326, 388, 391, 307, 264, 50244, 295, 264, 2808, 5359, 11, 293, 321, 366, 5404, 281, 2928, 702, 14943, 13, 50629, 50682, 6966, 307, 2221, 13, 2326, 388, 391, 311, 9060, 1570, 1880, 813, 702, 1871, 13, 50870, 50911, 634, 5112, 505, 300, 412, 341, 42729, 3196, 295, 264, 1064, 11, 365, 5272, 293, 12904, 9256, 450, 10539, 949, 505, 11, 51245, 51287, 1034, 4680, 10117, 490, 3936, 293, 1080, 3542, 5160, 881, 26336, 281, 264, 1575, 13, 51494, 51523, 634, 575, 12525, 22618, 1968, 6144, 35617, 1456, 397, 266, 311, 589, 307, 534, 10281, 934, 439, 11, 51799, 51815, 50365, 293, 393, 4411, 50431] ]) # fmt: on torch.testing.assert_close(generated_ids, EXPECTED_OUTPUT) EXPECTED_TRANSCRIPT = [ { "text": ( " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel." " Nor is Mr. Quilter's manner less interesting than his matter. He tells us that at this festive" " season of the year, with Christmas and roast beef looming before us, similes drawn from eating" " and its results occur most readily to the mind. He has grave doubts whether Sir Frederick " "Leighton's work is really Greek after all, and can discover" ), "offsets": [ { "text": ( " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel." ), "timestamp": (0.0, 5.28), }, { "text": " Nor is Mr. Quilter's manner less interesting than his matter.", "timestamp": (6.34, 10.1), }, { "text": ( " He tells us that at this festive season of the year, with Christmas and roast beef looming before us," ), "timestamp": (10.92, 17.6), }, { "text": (" similes drawn from eating and its results occur most readily to the mind."), "timestamp": (18.44, 22.580000000000002), }, { "text": ( " He has grave doubts whether Sir Frederick Leighton's work is really Greek after all," ), "timestamp": (23.16, 28.68), }, { "text": (" and can discover"), "timestamp": (28.68, 30.0), }, ], } ] transcript = processor.batch_decode(generated_ids, skip_special_tokens=True, output_offsets=True) self.assertEqual(transcript, EXPECTED_TRANSCRIPT) @slow def test_tiny_token_timestamp_generation(self): set_seed(0) processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") model.to(torch_device) model.generation_config.alignment_heads = [[2, 2], [3, 0], [3, 2], [3, 3], [3, 4], [3, 5]] input_speech = self._load_datasamples(4) input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features input_features = input_features.to(torch_device) generate_outputs = model.generate( input_features, max_length=448, return_timestamps=True, return_token_timestamps=True ) self.assertEqual(generate_outputs["sequences"].shape, generate_outputs["token_timestamps"].shape) # fmt: off EXPECTED_OUTPUT = torch.tensor([ [0.0000, 0.8200, 0.9800, 1.1200, 1.1200, 1.2200, 1.5000, 1.7200, 1.9800, 2.3400, 2.5000, 2.6600, 3.2000, 3.5600, 3.6800, 3.8000, 4.1000, 4.3000, 4.5800, 4.9400, 5.3800, 11.9000, 11.9000, 11.9000, 11.9000, 11.9000, 11.9000, 11.9000, 11.9000, 11.9000, 11.9000, 11.9000, 11.9000, 11.9000, 11.9000, 11.9000, 11.9000, 11.9000, 11.9000], [0.0000, 0.9000, 1.1400, 1.4200, 1.5200, 1.6600, 1.6600, 1.8800, 2.1000, 2.2200, 2.6200, 3.1400, 3.5800, 3.9400, 4.4000, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600, 17.9600], [0.0000, 0.7600, 1.0000, 1.4200, 1.8000, 1.9400, 2.1800, 2.5200, 3.0200, 3.3200, 3.5400, 3.9400, 4.5600, 4.9400, 5.2800, 5.5600, 5.9000, 6.1600, 6.3000, 6.4800, 6.4800, 6.6400, 7.8200, 7.9600, 8.2200, 8.6000, 8.9200, 9.2200, 9.5200, 9.7200, 10.0800, 10.5400, 10.8800, 11.2600, 11.5400, 11.7400, 12.0800, 16.6000, 16.6000], [0.0000, 0.7400, 1.0400, 1.3000, 1.6800, 2.1200, 2.4800, 2.7600, 3.0800, 3.1600, 3.4000, 3.6000, 4.0200, 4.2200, 4.8600, 5.2400, 5.7400, 6.3400, 6.6200, 6.7600, 6.7600, 6.8600, 7.2400, 7.4000, 7.6800, 7.9200, 8.4800, 8.7600, 9.2000, 9.2000, 9.4000, 15.8200, 15.8200, 15.8200, 15.8200, 15.8200, 15.8200, 15.8200, 15.8200] ]) # fmt: on torch.testing.assert_close(generate_outputs["token_timestamps"].to("cpu"), EXPECTED_OUTPUT) @slow def test_small_token_timestamp_generation(self): set_seed(0) processor = WhisperProcessor.from_pretrained("openai/whisper-small") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") model.to(torch_device) input_speech = self._load_datasamples(4) input_features = processor( input_speech, return_tensors="pt", sampling_rate=16_000, return_token_timestamps=True ) input_features = input_features.to(torch_device) generate_outputs = model.generate( **input_features, max_length=448, return_timestamps=True, return_token_timestamps=True ) self.assertEqual(generate_outputs["sequences"].shape, generate_outputs["token_timestamps"].shape) # fmt: off EXPECTED_OUTPUT = torch.tensor([ [0.0000, 0.7400, 0.8000, 0.9800, 1.0200, 1.1400, 1.4000, 1.5200, 1.9200, 2.2600, 2.3800, 2.5400, 2.8600, 3.2600, 3.3400, 3.4400, 3.6000, 3.6800, 3.9200, 4.2000, 4.4800, 4.7800, 5.2600, 5.8200, 5.8200, 5.8200, 5.8200, 5.8200, 5.8200, 5.8200, 5.8200, 5.8200, 5.8200, 5.8200, 5.8200, 5.8200, 5.8200, 5.8200, 5.8200, 5.8200, 5.8200], [0.0000, 0.7600, 0.9800, 1.3000, 1.3800, 1.5200, 1.5800, 1.7000, 1.8400, 2.1000, 2.5000, 3.1400, 3.4400, 3.7400, 4.2000, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800, 4.7800], [0.0000, 0.6600, 0.9000, 1.2200, 1.5200, 1.7600, 2.0200, 2.4000, 2.9200, 3.1800, 3.3200, 3.6200, 4.1000, 4.3600, 4.7800, 5.1200, 5.3400, 5.7200, 6.0600, 6.2000, 6.2000, 6.2000, 6.5000, 6.9000, 7.6400, 8.0000, 8.2400, 8.5200, 8.7400, 9.0800, 9.4000, 9.5400, 9.9400, 10.4200, 10.7600, 11.1200, 11.4400, 11.5800, 11.8600, 12.4600, 12.4600], [0.0000, 0.6600, 0.8600, 1.1400, 1.5000, 1.9600, 2.3600, 2.6400, 2.9800, 3.1200, 3.2400, 3.4800, 3.7800, 4.1600, 4.6400, 5.0800, 5.4400, 6.2200, 6.2200, 6.2200, 6.4000, 6.8400, 7.1200, 7.2600, 7.4800, 7.8200, 8.1400, 8.7000, 9.0200, 9.0200, 9.2000, 9.8800, 9.8800, 9.8800, 9.8800, 9.8800, 9.8800, 9.8800, 9.8800, 9.8800, 9.8800] ]) # fmt: on torch.testing.assert_close(generate_outputs["token_timestamps"].to("cpu"), EXPECTED_OUTPUT) @slow def test_tiny_token_timestamp_batch_generation(self): set_seed(0) processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") model.to(torch_device) model.generation_config.alignment_heads = [[2, 2], [3, 0], [3, 2], [3, 3], [3, 4], [3, 5]] num_samples = 4 num_return_sequences = 2 input_speech = self._load_datasamples(num_samples) input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features input_features = input_features.to(torch_device) generate_outputs = model.generate( input_features, max_length=448, return_timestamps=True, return_token_timestamps=True, num_beams=3, num_return_sequences=num_return_sequences, ) # task id and lang id prompts should not have timestamp tokens self.assertEqual(len(generate_outputs["sequences"]), num_return_sequences * num_samples) @slow def test_tiny_token_timestamp_generation_longform(self): set_seed(0) processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") model.to(torch_device) model.generation_config.alignment_heads = [[2, 2], [3, 0], [3, 2], [3, 3], [3, 4], [3, 5]] input_speech = self._load_datasamples(5) long_input_speech = np.concatenate(input_speech, dtype=np.float32) inputs = processor( long_input_speech, return_tensors="pt", truncation=False, # False so the audio isn't truncated and whole audio is sent to the model return_attention_mask=True, padding=True, ) inputs = inputs.to(torch_device) generate_outputs = model.generate( **inputs, return_segments=True, return_token_timestamps=True, return_timestamps=True ) token_timestamps_shape = [ [segment["token_timestamps"].shape for segment in segment_list] for segment_list in generate_outputs["segments"] ] tokens_shape = [ [segment["tokens"].shape for segment in segment_list] for segment_list in generate_outputs["segments"] ] self.assertListEqual(tokens_shape, token_timestamps_shape) # fmt: off EXPECTED_OUTPUT = [ torch.tensor([0.0000, 0.8200, 0.9400, 1.1200, 1.1200, 1.2200, 1.5000, 1.7200, 2.0400, 2.3400, 2.5000, 2.6600, 3.2000, 3.4400, 3.5600, 3.6800, 3.8200, 4.1000, 4.3000, 4.5800, 4.9400, 5.4000, 6.3600, 6.5400]), torch.tensor([6.5400, 6.7400, 6.9600, 7.2600, 7.3400, 7.5800, 7.5800, 7.6400, 7.8400, 8.1000, 8.5000, 9.0000, 9.4800, 9.7200, 10.2600, 11.1000, 11.2200]), torch.tensor([11.2200, 11.4200, 11.6600, 12.0800, 12.4400, 12.5800, 12.8400, 13.1600, 13.6800, 14.0000, 14.2200, 14.6200, 14.9800, 15.2200, 15.6000, 15.9400, 16.2000, 16.5600, 16.8400, 16.9800, 16.9800]), torch.tensor([16.9800, 17.3200, 18.1800, 18.6400, 18.8600, 19.2800, 19.5600, 19.8800, 20.1800, 20.3800, 20.7200, 21.1600, 21.5400, 21.9000, 22.2000, 22.4200, 22.8400, 23.7000, 23.7000]), torch.tensor([23.7000, 23.9400, 24.1800, 24.3800, 24.8400, 25.2800, 25.6600, 25.9200, 26.2600, 26.3800, 26.5800, 26.7600, 27.1600, 27.3800, 28.0400, 28.3800, 28.8200, 29.3400, 29.5200, 29.9800, 29.9800]), torch.tensor([29.4400, 29.7000, 30.0600, 30.3800, 30.5400, 30.8200, 31.0600, 31.6600, 31.9200, 32.3000, 32.5000, 32.6200, 33.6800, 33.8000]), torch.tensor([33.8000, 33.9800, 33.9800, 34.1800, 34.4400, 34.6200, 35.0000, 35.2200, 35.3200, 35.5600, 35.9200, 36.3800, 36.6200, 36.6600, 36.9600, 37.3400, 37.9800, 38.5800, 38.7200, 38.9800, 39.4400, 39.5800, 39.8000, 40.1200, 40.2600, 40.5200]), torch.tensor([40.5200, 40.6200, 41.1000, 41.5400, 41.9200, 42.1000, 42.3200, 42.3200, 43.0600, 44.6000, 44.7000]), torch.tensor([44.7000, 44.8600, 44.9400, 45.1400, 45.1400, 45.2800, 45.6200, 45.9000, 46.2600, 47.1600, 47.4800, 47.7400, 48.1000, 48.2800, 48.4000, 48.6200, 48.8400, 49.0400, 49.2800, 49.4800, 49.6600, 49.9400, 50.5400, 50.5400]), torch.tensor([50.5400, 50.6600, 50.8800, 51.2400, 51.7200, 52.8400, 52.9600]), torch.tensor([52.9600, 53.0400, 53.2600, 53.4200, 53.5800, 53.9200, 54.1200, 54.7200, 54.9400, 55.2600, 55.6200, 55.9800, 56.5600, 56.8000, 56.9200, 57.3600, 57.9200, 58.1600, 58.5200, 58.6400, 58.8200, 59.4200, 59.4200]), torch.tensor([58.6800, 59.1400, 59.5400, 59.9200, 60.1400, 60.3800, 60.8400, 61.6000, 62.2400, 62.4200, 62.4200]) ] # fmt: on for segment, exp_segment in zip(generate_outputs["segments"][0], EXPECTED_OUTPUT): torch.testing.assert_close(segment["token_timestamps"], exp_segment) @slow def test_tiny_specaugment_librispeech(self): torch_device = "cpu" set_seed(0) # Apply SpecAugment model = WhisperModel.from_pretrained("openai/whisper-tiny", apply_spec_augment=True) # Set model to training mode to enable SpecAugment model.train() model.to(torch_device) input_speech = self._load_datasamples(1) feature_extractor = WhisperFeatureExtractor() input_features = feature_extractor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features with torch.no_grad(): logits = model( input_features, decoder_input_ids=torch.tensor([[50258, 50259, 50359]]), output_hidden_states=False, output_attentions=False, return_dict=False, use_cache=False, ) # fmt: off EXPECTED_LOGITS = torch.tensor( [ 0.9362, -4.7105, 5.0879, 3.9642, 1.0013, -6.0096, 4.7285, -3.1847, -0.8648, 1.9631, 6.2653, 3.6936, 0.3575, -4.5818, 3.0564, 7.8712, 2.9951, 0.6848, 9.9497, -2.6638, 1.1571, -6.8546, -1.4333, -7.7584, 1.1200, 3.9030, 4.4655, -4.4919, -1.1703, 9.6241 ] ) # fmt: on torch.testing.assert_close(logits[0][0, 0, :30].cpu(), EXPECTED_LOGITS, rtol=1e-4, atol=1e-4) @slow def test_generate_with_prompt_ids(self): processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") model.to(torch_device) input_speech = self._load_datasamples(4)[-1:] input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features input_features = input_features.to(torch_device) output_without_prompt = model.generate(input_features) prompt_ids = processor.get_prompt_ids("Leighton", return_tensors="pt").to(torch_device) output_with_prompt = model.generate(input_features, prompt_ids=prompt_ids) expected_without_prompt = " He has grave doubts whether Sir Frederick Layton's work is really Greek after all and can discover in it but little of Rocky Ithaca." expected_with_prompt = " He has grave doubts whether Sir Frederick Leighton's work is really Greek after all and can discover in it but little of Rocky Ithaca." output_without_prompt = processor.decode(output_without_prompt[0]) output_with_prompt = processor.decode(output_with_prompt[0]) self.assertEqual(output_without_prompt, expected_without_prompt) self.assertEqual(output_with_prompt, expected_with_prompt) @slow def test_generate_with_forced_decoder_ids(self): processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") model.to(torch_device) ds = load_dataset("facebook/multilingual_librispeech", "german", split="test", streaming=True) ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000)) input_speech = next(iter(ds))["audio"]["array"] input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features input_features = input_features.to(torch_device) forced_decoder_ids = processor.get_decoder_prompt_ids( task="transcribe", language="german", ) generated_ids = model.generate(input_features, do_sample=False, language="<|de|>", task="transcribe") generated_ids_forced = model.generate(input_features, do_sample=False, forced_decoder_ids=forced_decoder_ids) self.assertListEqual(generated_ids.tolist()[0], generated_ids_forced.tolist()[0]) @slow def test_generate_with_prompt_ids_task_language(self): EXPECTED_TEXT = " Mr. Kilter is the apostle of the middle classes and we are glad to welcome his gospel." processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") model = model.to(torch_device) prompt = "Mr. Kilter, Brionno." # let's force Quilter -> Kilter, Brion -> Brionno prompt_ids = processor.get_prompt_ids(prompt, return_tensors="pt").to(torch_device) ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation[:-1]") ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000)) input_speech = ds[0]["audio"]["array"] input_features = processor( input_speech, return_tensors="pt", truncation=False, padding="longest", sampling_rate=16_000 )["input_features"] input_features = input_features.to(device=torch_device) gen_kwargs = { "do_sample": False, "return_timestamps": True, "language": "english", "task": "transcribe", "prompt_ids": prompt_ids, } generated_ids = model.generate(input_features, **gen_kwargs) transcription = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] self.assertEqual(transcription, EXPECTED_TEXT) @slow def test_language_detection(self): processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") model.to(torch_device) input_speech = self._load_datasamples(4)[-1:] input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features input_features = input_features.to(torch_device) lang_id = model.detect_language(input_features)[0].item() ids_to_lang = {v: k for k, v in model.generation_config.lang_to_id.items()} self.assertEqual(ids_to_lang[lang_id], "<|en|>") audio = hf_hub_download("Narsil/asr_dummy", filename="hindi.ogg", repo_type="dataset") raw_audio, sr = torchaudio.load(audio) input_speech = torchaudio.transforms.Resample(sr, 16_000)(raw_audio).numpy() input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features input_features = input_features.to(torch_device) lang_id = model.detect_language(input_features)[0].item() self.assertEqual(ids_to_lang[lang_id], "<|hi|>") @slow def test_default_multilingual_transcription_short_form(self): processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") model.to(torch_device) audio = hf_hub_download("Narsil/asr_dummy", filename="hindi.ogg", repo_type="dataset") raw_audio, sr = torchaudio.load(audio) input_speech = torchaudio.transforms.Resample(sr, 16_000)(raw_audio).numpy() input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features input_features = input_features.to(torch_device) # task defaults to transcribe sequences = model.generate(input_features) transcription = processor.batch_decode(sequences, skip_special_tokens=False)[0] self.assertEqual(transcription, " Mirchi mein ki tene vibinda prajatiya hai") # set task to translate sequences = model.generate(input_features, task="translate") transcription = processor.batch_decode(sequences, skip_special_tokens=False)[0] self.assertEqual(transcription, " How much is the difference between the girls?") @slow def test_default_multilingual_transcription_long_form(self): processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2") model.to(torch_device) audio = hf_hub_download("Narsil/asr_dummy", filename="hindi.ogg", repo_type="dataset") raw_audio, sr = torchaudio.load(audio) input_speech = torchaudio.transforms.Resample(sr, 16_000)(raw_audio) input_speech = input_speech.repeat(1, 10).numpy() input_features = processor( input_speech, return_tensors="pt", padding="longest", truncation=False, sampling_rate=16_000 ).input_features.to(torch_device) # task defaults to transcribe sequences = model.generate(input_features, return_timestamps=True) transcription = processor.batch_decode(sequences, skip_special_tokens=False)[0] self.assertEqual(transcription, " मिर्ची में कितने विबिन्द प्रजातियां हैं? मिर्ची में कितने विबिन्द प्रजातियां हैं?") # set task to translate sequences = model.generate(input_features, task="translate", return_timestamps=True) transcription = processor.batch_decode(sequences, skip_special_tokens=False)[0] self.assertEqual( transcription, " How many different species are there in the chilli? How many different species are there in the chilli?", ) @slow @require_torch_accelerator def test_speculative_decoding_distil(self): dtype = torch.float16 if (torch.cuda.is_available() or is_torch_xpu_available()) else torch.float32 model_id = "openai/whisper-large-v2" model = WhisperForConditionalGeneration.from_pretrained(model_id, dtype=dtype, use_safetensors=True) model.to(torch_device) processor = WhisperProcessor.from_pretrained(model_id) assistant_model_id = "distil-whisper/distil-large-v2" assistant_model = WhisperForCausalLM.from_pretrained(assistant_model_id, dtype=dtype, use_safetensors=True) assistant_model.to(torch_device) dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") sample = dataset[0]["audio"] input_features = processor(sample["array"], return_tensors="pt", sampling_rate=16_000).input_features input_features = input_features.to(torch_device, dtype=torch.float16) # warm up assisted decoding _ = model.generate(input_features, assistant_model=assistant_model) # warm up non-assisted decoding _ = model.generate(input_features) # assisted decoding start_time = time.time() tokens = model.generate(input_features, assistant_model=assistant_model) total_time_assist = time.time() - start_time transcription_ass = processor.batch_decode(tokens, skip_special_tokens=True) # non-assisted decoding start_time = time.time() tokens = model.generate(input_features) total_time_non_assist = time.time() - start_time transcription_non_ass = processor.batch_decode(tokens, skip_special_tokens=True) self.assertEqual(transcription_ass, transcription_non_ass) self.assertEqual( transcription_ass, [" Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel."], ) self.assertTrue(total_time_non_assist > total_time_assist, "Make sure that assistant decoding is faster") @slow @require_torch_accelerator def test_speculative_decoding_non_distil(self): dtype = torch.float16 if torch_device in ["cuda", "xpu"] else torch.float32 model_id = "openai/whisper-large-v2" model = WhisperForConditionalGeneration.from_pretrained(model_id, dtype=dtype, use_safetensors=True) model.to(torch_device) processor = WhisperProcessor.from_pretrained(model_id) assistant_model_id = "openai/whisper-tiny" assistant_model = WhisperForConditionalGeneration.from_pretrained( assistant_model_id, dtype=dtype, use_safetensors=True ) assistant_model.to(torch_device) dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") sample = dataset[0]["audio"] input_features = processor(sample["array"], return_tensors="pt", sampling_rate=16_000).input_features input_features = input_features.to(torch_device, dtype=torch.float16) # warm up assisted decoding _ = model.generate(input_features, assistant_model=assistant_model) # warm up non-assisted decoding _ = model.generate(input_features) # assisted decoding start_time = time.time() tokens = model.generate(input_features, assistant_model=assistant_model) total_time_assist = time.time() - start_time transcription_ass = processor.batch_decode(tokens, skip_special_tokens=True) # non-assisted decoding start_time = time.time() tokens = model.generate(input_features) total_time_non_assist = time.time() - start_time transcription_non_ass = processor.batch_decode(tokens, skip_special_tokens=True) self.assertEqual(transcription_ass, transcription_non_ass) self.assertEqual( transcription_ass, [" Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel."], ) self.assertTrue(total_time_non_assist > total_time_assist, "Make sure that assistant decoding is faster") @slow def test_whisper_longform_single_batch(self): # fmt: off EXPECTED_TEXT = [" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel. Nor is Mr. Quilter's manner less interesting than his matter. He tells us that at this festive season of the year, with Christmas and roast beef looming before us, similes drawn from eating and its results occur most readily to the mind. He has grave doubts whether Sir Frederick Layton's work is really Greek after all, and can discover in it but little of rocky Ithaca. Linnell's pictures are a sort of up-gards and atom paintings, and Mason's exquisite idles are as national as a jingo poem. Mr. Birk at Foster's landscapes smile at one much in the same way that Mr. Carker used to flash his teeth. Mr. John Collier gives his sitter a cheerful slap in the back, before he says, like a shampoo or a Turkish bath. Next man, it is obviously unnecessary for us to point out how luminous these criticisms are, how delicate an expression. On the general principles of art, Mr. Quilter writes with equal lucidity. he tells us is of a different quality to mathematics, and finish in art is adding more effect. As for etchings, there are two kinds, British and foreign. He laments most bitterly the divorce that has been made between decorative art and what we usually call pictures. Makes the customary appeal to the last judgment and reminds us that in the great days of art Michelangelo was the furnishing upholsterer. Near the fire, any ornaments Fred brought home from India on the mantelboard. In fact, he is quite severe on Mr. Ruskin for not recognizing that a picture should denote the frailty of man. And remarks was pleasing courtesy in Felicitis Grace that many faces are feeling. Only, unfortunately, his own work never does get good. Mr. Quilter has missed his chance, for he has failed even to make himself the Tupper of painting. By Harry Quilter M.A. Because you were sleeping instead of conquering, the lovely rose princess has become a fiddle without a bow, while poor Shaggy sits there, accoing dove. He has gone and gone for good, answered Polychrome, would manage to squeeze into the room beside the dragon and had witnessed the occurrences with much interest. I have remained a prisoner only because I wished to be one. And with this, he stepped forward and burst the stout chains as easily as if they had been threads. The little girl had been asleep, but she heard the wraps and opened the door. The king has fled and disgraced and your friends are asking for you. I begged Ruggadot long ago to send him away, but he would not do so. I also offered to help your brother to escape, but he would not go. He eats and sleeps very steadily, replied the new king. I hope he doesn't work too hard, since Shaggy. He doesn't work at all. In fact, there's nothing he can do in these dominions, as well as our gnomes, whose numbers are so great that it worries us to keep them all busy. Not exactly, we've turned Calico. Where is my brother now? In Quared Shaggy. In the metal forest. Where is that? The metal forest is in the great domed cavern, the largest and all-ard dominions, replied Calico. Calico hesitated. However, if we look sharp, we may be able to discover one of these secret ways. Oh no, I'm quite sure he didn't. That's funny, remarked Betsy thoughtfully. I don't believe and knew any magic or she'd have worked it before. I do not know, confess shaggy. True, a great calico. Calico went to the big gong and pounded on it just as we're good to use to do, but no one answered the summons. Having returned to the Royal Cavern, Calico first pounded the gong and then sat in the throne, wearing ruggedos discarded ruby crown and holding in his hand to scepter which ruggedo had so often thrown at his head. A man said to the universe, Sir, I exist. Sweat covered Breon's body, trickling into the titling cloth that was the only german he wore. The cut on his chest still dripping blood. The ache of his overstrained eyes, even the soaring arena around him with thousands of spectators, retrovealities not worth thinking about. His instant panic was followed by a small sharp blow high on his chest. One minute, a voice said, and a time buzzer sounded. A minute is not a very large measure of time, and his body needed every fraction of it. The buzzers were triggered as muscles into complete relaxation. Oli's heart and lungs worked on at a strong, measured rate. He was in reverie, sliding along the borders of consciousness. The contestants in the 20s needed undisturbed rest. Therefore, nights in the dormitories were as quiet as death. Particularly so, on this last night, when only two of the little cubicles were occupied, The thousands of others standing with dark empty doors. The other voice snapped with a harsh urgency, clearly used to command. I'm here because the matter is of utmost importance, and brand is the one I must see. Now stand aside. The twenties, he must have drawn his gun because the intruder said quickly, but that away you're being a fool. out, there was silence then, and still wondering, Breon was once more asleep. Ten seconds, he asked the handler who was needing his aching muscles. A red-haired mountain of a man, with an apparently inexhaustible store of energy. There could be little art in this last and final round of fencing. Just thrust and parry, and victory to the stronger. a man who entered the twenties had his own training tricks. They were appeared to be an immediate association with the death trauma, as if the two were inextricably linked into one. The strength that enables someone in a trance to hold his body stiff and unsupported except at two points, the head and heels. This is physically impossible when conscious. had died before during the 20s and death during the last round was in some ways easier than defeat. Breathing deeply, Breon's softly spoke the auto-hypnotic phrases that triggered the process. When the buzzer sounded, he pulled his foil from his second startled grasp and ran forward. Our role looked amazed at the sudden fury of the attack, then smiled. He thought it was the last burst of energy. He knew how close they both were to exhaustion. Breon saw something close to panic on his opponent's face when the man finally recognized his error. A wave of despair rolled out from our rogue. Breon sensed it and knew the fifth point was his. the powerful twist that's rest of the side, in and under the guard."] # fmt: on processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") model = model.to(torch_device) ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean") one_audio = np.concatenate([x["array"] for x in ds["validation"]["audio"]], dtype=np.float32) input_features = processor( one_audio, return_tensors="pt", truncation=False, padding="longest", sampling_rate=16_000 )["input_features"] input_features = input_features.to(device=torch_device) result = model.generate(input_features, return_timestamps=True) decoded = processor.batch_decode(result, skip_special_tokens=True) self.assertEqual(decoded, EXPECTED_TEXT) decoded_with_timestamps = processor.batch_decode(result, skip_special_tokens=True, decode_with_timestamps=True) no_timestamp_matches = re.split(r"<\|[\d\.]+\|>", decoded_with_timestamps[0]) self.assertEqual(["".join(no_timestamp_matches)], EXPECTED_TEXT) timestamp_matches = re.findall(r"<\|[\d\.]+\|>", decoded_with_timestamps[0]) timestamp_floats = [float(t[2:-2]) for t in timestamp_matches] is_increasing = all(timestamp_floats[i] <= timestamp_floats[i + 1] for i in range(len(timestamp_floats) - 1)) self.assertTrue(is_increasing) @slow def test_whisper_longform_prompt_ids(self): processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") model = model.to(torch_device) prompt = "Mr. Kilter, Brionno." # let's force Quilter -> Kilter, Brion -> Brionno prompt_ids = processor.get_prompt_ids(prompt, return_tensors="pt").to(torch_device) ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation[:-1]") one_audio = np.concatenate([x["array"] for x in ds["audio"]], dtype=np.float32) first_text = ds[0]["text"].lower() last_text = ds[-1]["text"].lower() input_features = processor( one_audio, return_tensors="pt", truncation=False, padding="longest", sampling_rate=16_000 )["input_features"] input_features = input_features.to(device=torch_device) result = model.generate( input_features, prompt_ids=prompt_ids, return_timestamps=True, prompt_condition_type="first-segment", condition_on_prev_tokens=True, ) decoded_first_segment = processor.batch_decode(result, skip_special_tokens=True) result = model.generate( input_features, prompt_ids=prompt_ids, return_timestamps=True, prompt_condition_type="all-segments", condition_on_prev_tokens=True, ) decoded_all_segments = processor.batch_decode(result, skip_special_tokens=True) # show that first segment has quilter and last segment has brion self.assertIn("quilter", first_text) self.assertIn("brion", last_text) # condition on first segment correctly changes to kilter in first segment, but does not transcribe "brianno" correctly self.assertIn("kilter", decoded_first_segment[0][: len(first_text)].lower()) self.assertNotIn("brionno", decoded_first_segment[0][-len(last_text) :].lower()) # condition on all-segment correctly changes to kilter in first segment and correctly transcribes "brianno" self.assertIn("kilter", decoded_all_segments[0][: len(first_text)].lower()) self.assertIn("brionno", decoded_all_segments[0][-len(last_text) :].lower()) @slow def test_whisper_longform_single_batch_prev_cond(self): # fmt: off EXPECTED_TEXT = [" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel. Nor is Mr. Quilter's manner less interesting than his matter. He tells us that at this festive season of the year, with Christmas and roast beef looming before us, similes drawn from eating and its results occur most readily to the mind. He has grieved doubts whether Sir Frederick Layton's work is really Greek after all, and can discover in it but little of rocky Ithaca. Linnell's pictures are a sort of up-gards and atom paintings, and Mason's exquisite itals are as national as a jingo poem. Mr. Birk at Foster's landscapes smile at one much in the same way that Mr. Carker used to flash his teeth. When Mr. John Collier gives his sitter a cheerful slap in the back, before he says like a shampooer and a Turkish bath, next man it is obviously unnecessary for us to point out how luminous these criticisms are, how delicate an expression. On the general principles of art, Mr. Quilter writes with equal lucidity. He tells us is of a different quality to mathematics, and finish in art is adding more effect. As for etchings, there are two kinds, British and foreign. He laments most bitterly the divorce that has been made between decorative art and what we usually call pictures. Makes a customary appeal to the last judgment and reminds us that in the great days of art Michelangelo was the furnishing upholsterer. Near the fire, any ornaments Fred brought home from India on the mental board. In fact, he is quite severe on Mr. Ruskin for not recognizing that a picture should denote the frailty of man, and remarks was pleasing courtesy in felicitous grace that many faces are feeling. Unfortunately his own work never does get good. Mr. Quilter has missed his chance, for he has failed even to make himself the tupper of painting. By Harry Quilter M.A. because he was sleeping instead of conquering, the lovely rose princess has become a fiddle without a bow, while poor Shaggy sits there, accooing dove. He has gone and gone for good. answered Polychrome, who had managed to squeeze into the room beside the dragon, and had witnessed the occurrences with much interest. I have remained a prisoner only because I wished to be one. And with this he stepped forward and burst the stout chains as easily as if they had been threads. The little girl had been asleep, but she heard the wraps and opened the door. The king has fled and disgraced and your friends are asking for you. I begged Ruggido long ago to send him away, but he would not do so. I also offered to help your brother to escape, but he would not go. He eats and sleeps very steadily, replied the new king. I hope he doesn't work too hard, since Shaggy. He doesn't work at all. In fact, there is nothing he can do in these dominions, as well as our gnomes, whose numbers are so great that it worries us to keep them all busy. Not exactly, we've turned Calico. Where is my brother now, inquired Shaggy. In the metal forest. Where is that? The metal forest is in the great domed cavern, the largest in all our dominions, replied Calico. Calico hesitated. However, if we look sharp, we may be able to discover one of these secret ways. Oh no, I'm quite sure he didn't. It's funny, remarked Betsy thoughtfully. I don't believe and knew any magic, or she'd have worked it before. I do not know, confessed Shaggy. True, agreed Calico. Calico went to the big gong and pounded on it, just as Ruggido used to do, but no one answered the summons. Having returned to the royal cavern, Calico first pounded the gong and then sat in the throne, wearing Ruggido's discarded ruby crown. And holding it in his hand, the scepter which Ruggido had so often thrown at his head. A man said to the universe, Sir, I exist. Sweat covered Breon's body, trickling into the titling cloth that was the only german he wore. The cut on his chest, still dripping blood. The ache of his overstrained eyes, even to soaring arena around him with thousands of spectators, retrovealities not worth thinking about. His instant panic was followed by a small sharp blow high on his chest. One minute, a voice said, and a time buzzer sounded. A minute is not a very large measure of time, and his body needed every fraction of it. The buzzers were triggered as muscles into complete relaxation. Only his heart and lungs worked on at a strong measured rate. He was in reverie, sliding along the borders of consciousness. The contestants in the twenties needed undisturbed rest. Therefore, nights in the dormitories were as quiet as death. Particularly so, on this last night, when only two of the little cubicles were occupied, the thousands of others standing with dark empty doors. The other voice snapped with a harsh urgency clearly used to command. I'm here because the matter is of utmost importance, and brand is the one I must see. Now stand aside. The twenties, he must have drawn his gun because the intruder said quickly, but that away you're being a fool. Out there was silence then, and still wondering, Breon was once more asleep. In seconds he asked the handler who was needing his aching muscles. A red-haired mountain of a man with an apparently inexhaustible store of energy. There could be little art in this last and final round of fencing. Just thrust and parry and victory to the stronger. Every man who entered the twenties had his own training tricks. There appeared to be an immediate association with the death trauma, as if the two were inextricably linked into one. The strength that enables someone in a trance to hold his body stiff and unsupported, except at two points, the head and heels. This is physically impossible when conscious. Others had died before during the twenties and death during the last round was, in some ways, easier than defeat. In deeply, Breon softly spoke the auto-hypnotic phrases that triggered the process. When the buzzer sounded, he pulled his foil from his second startled grasp and ran forward. Our role looked amazed at the sudden fury of the attack, then smiled. He thought it was the last burst of energy. He knew how close they both were to exhaustion. Breon saw something close to panic on his opponent's face when the man finally recognized his error. A wave of despair rolled out from our rogue. Breon sensed it and knew the fifth point was his. Then the powerful twist that's rested aside, in and under the guard."] # fmt: on processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") model = model.to(torch_device) ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean") one_audio = np.concatenate([x["array"] for x in ds["validation"]["audio"]], dtype=np.float32) input_features = processor( one_audio, return_tensors="pt", truncation=False, padding="longest", sampling_rate=16_000 )["input_features"] input_features = input_features.to(device=torch_device) gen_kwargs = { "return_timestamps": True, "no_speech_threshold": 0.6, "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), "compression_ratio_threshold": 1.35, "condition_on_prev_tokens": True, "logprob_threshold": -1.0, } torch.manual_seed(0) result = model.generate(input_features, **gen_kwargs) decoded = processor.batch_decode(result, skip_special_tokens=True) self.assertEqual(decoded, EXPECTED_TEXT) @slow def test_whisper_shortform_single_batch_prev_cond(self): # fmt: off xpu_expectation = [" Folks, I spend a lot of time right over there, night after night after night, actually. Carefully selecting for you the day's noosiest, most aerodynamic headlines, stress testing, and those topical anti-lock breaks and power steering, painstakingly stitching, leather seating, so soft, it would make JD power and her associates blush to create the luxury sedan that is my nightly monologue. But sometimes, you sometimes, folks, I lurched a consciousness in the back of an abandoned school bus and slap myself awake."] cuda_expectation = [" Folks, I spend a lot of time right over there, night after night after night, actually. Carefully selecting for you the day's noosiest, most aerodynamic headlines, stress testing, and those topical anti-lock breaks and power steering, painstakingly stitching, leather seating so soft, it would make JD power and her associates blush to create the luxury sedan that is my nightly monologue. But sometimes, you sometimes, folks. I lurched a consciousness in the back of an abandoned school bus and slap myself awake."] cuda_expectation2 = [" Folks, I spend a lot of time right over there, night after night after night, actually. Carefully selecting for you the day's noosiest, most aerodynamic headlines, stress testing, and those topical anti-lock breaks and power steering, painstakingly stitching, leather seating so soft, it would make JD power and her associates blush to create the luxury sedan that is my nightly monologue. But sometimes, you sometimes, folks. I lurched a consciousness in the back of an abandoned school bus and slap myself a wig."] rocm_expectation = [" Folks, I spend a lot of time right over there, night after night after night, actually. Carefully selecting for you the day's noosiest, most aerodynamic headlines, stress testing, and those topical anti-lock breaks and power steering, painstakingly stitching, leather seating, so soft, it would make JD power and her associates blush to create the luxury sedan that is my nightly monologue. But sometimes, you sometimes, folks, I lurched a consciousness in the back of an abandoned school bus and slap myself awake."] # fmt: on expected_output = Expectations( {("xpu", None): xpu_expectation, ("cuda", None): cuda_expectation, ("rocm", (9, 4)): rocm_expectation} ).get_expectation() expected_output2 = Expectations( {("xpu", None): xpu_expectation, ("cuda", None): cuda_expectation2, ("rocm", (9, 4)): rocm_expectation} ).get_expectation() processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") model = model.to(torch_device) ds = load_dataset("distil-whisper/meanwhile", "default")["test"] dataset = ds.cast_column("audio", Audio(sampling_rate=16000)) one_audio = dataset[1]["audio"]["array"] input_features = processor(one_audio, return_tensors="pt", sampling_rate=16_000)["input_features"] input_features = input_features.to(device=torch_device) gen_kwargs = { "return_timestamps": True, "no_speech_threshold": 0.6, "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), "compression_ratio_threshold": 1.35, "condition_on_prev_tokens": True, "logprob_threshold": -1.0, } torch.manual_seed(0) result = model.generate(input_features, **gen_kwargs) decoded = processor.batch_decode(result, skip_special_tokens=True) self.assertEqual(decoded, expected_output) gen_kwargs = { "return_timestamps": True, "no_speech_threshold": 0.3, "temperature": (0.0, 0.2), "compression_ratio_threshold": 1, "condition_on_prev_tokens": False, "logprob_threshold": -1.0, } torch.manual_seed(0) result = model.generate(input_features, **gen_kwargs) decoded = processor.batch_decode(result, skip_special_tokens=True) self.assertEqual(decoded, expected_output2) @slow def test_whisper_longform_single_batch_beam(self): # fmt: off EXPECTED_TEXT = [" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel. Nor is Mr. Quilter's manner less interesting than his matter. He tells us that at this festive season of the year, with Christmas and roast beef looming before us, similes drawn from eating and its results occur most readily to the mind. He has grave doubts whether Sir Frederick Layton's work is really Greek after all, and can discover in it but little of rocky Ithaca. Linnell's pictures are a sort of up-gards and atom paintings, and Mason's exquisite idles are as national as a jingo poem. Mr. Burkett Foster's landscapes smile at one much in the same way that Mr. Carker used to flash his teeth. When Mr. John Collier gives his sitter a cheerful slap in the back, before he says, like a shampooer and a Turkish bath, next man, it is obviously unnecessary for us to point out how luminous these criticisms are, how delicate an expression. On the general principles of art, Mr. Quilter writes with equal lucidity. He tells us is of a different quality to mathematics, and finish in art is adding more effect. As for etchings, there are two kinds, British and foreign. He laments most bitterly the divorce that has been made between decorative art and what we usually call pictures. Mix a customary appeal to the last judgment and reminds us that in the great days of art with Michelangelo was the furnishing upholsterer. Near the fire, any ornaments Fred brought home from India on the mental board. In fact, he is quite severe on Mr. Ruskin for not recognizing that a picture should denote the frailty of man, and remarks was pleasing courtesy in felicitous grace that many faces are feeling. Only, unfortunately, his own work never does get good. Mr. Quilter has missed his chance, for he has failed even to make himself the topper of painting. By Harry Quilter, M.A. Because he was sleeping instead of conquering, the lovely rose princess has become a fiddle without a bow, while poor Shaggy sits there, accooing dove. He has gone and gone for good, answered polychrome, who had managed to squeeze into the room beside the dragon, and had witnessed the occurrences with much interest. I have remained a prisoner only because I wished to be one. And with this, he stepped forward and burst the stout chains as easily as if they had been threads. The little girl had been asleep, but she heard the wraps and opened the door. The king has fled and disgraced, and your friends are asking for you. I begged Ruggado long ago to send him away, but he would not do so. I also offered to help your brother to escape, but he would not go. He eats and sleeps very steadily, replied the new king. I hope he doesn't work too hard, since Shaggy. He doesn't work at all. In fact, there is nothing he can do in these dominions, as well as our gnomes, whose numbers are so great that it worries us to keep them all busy. Not exactly, we've turned Calico. Where is my brother now, inquired Shaggy. In the metal forest. Where is that? The metal forest is in the great domed cavern, the largest in all our dominions, replied Calico. Calico hesitated. However, if we look sharp, we may be able to discover one of these secret ways. Oh no, I'm quite sure he didn't. That's funny, remarked Betsy thoughtfully. I don't believe and knew any magic, or she'd have worked it before. I do not know, confessed Shaggy. True, a great Calico. Calico went to the big gong and pounded on it, just as Ruggado used to do, but no one answered the summons. Having returned to the Royal Cavern, Calico first pounded the gong and then sat in the throne, wearing Ruggado's discarded ruby crown, and holding in his hand to scepter which Ruggado had so often thrown at his head. A man said to the universe, Sir, I exist. Sweat covered Breon's body, trickling into the tight-laying cloth that was the only germany war. The cut on his chest, still dripping blood. The ache of his overstrained eyes, even the soaring arena around him with thousands of spectators, retrovealities not worth thinking about. His instant panic was followed by a small sharp blow high on his chest. One minute, a voice said, and a time buzzer sounded. A minute is not a very large measure of time, and his body needed every fraction of it. The buzzers were, triggered his muscles into complete relaxation. Oli's heart and lungs worked on at a strong, measured rate. He was in reverie, sliding along the borders of consciousness. The contestants in the 20s needed undisturbed rest, therefore nights in the dormitories were as quiet as death. Particularly so, on this last night, when only two of the little cubicles were occupied, the thousands of others standing with dark empty doors. The other voice snapped with a harsh urgency clearly used to command. I'm here because the matter is of utmost importance, and brand is the one I must see. Now stand aside. The 20s, he must have drawn his gun because the intruder said quickly, but that away, there'd be no fool. Out, there was silence then, and still wondering, Brienne was once more asleep. Ten seconds, he asked the handler who was needing his aching muscles. A red-haired mountain of a man with an apparently inexhaustible store of energy. There could be little art in this last and final round of fencing, just thrust and parry and victory to the stronger. Every man who entered the 20s had his own training tricks. There appeared to be an immediate association with the death trauma, as if the two were inextricably linked into one. The strength that enables someone in a trance to hold his body stiff and unsupported except at two points, the head and heels. This is physically impossible when conscious. This had died before during the 20s and death during the last round was, in some ways, easier than defeat. Breathing deeply, Brienne softly spoke the auto-hypnotic phrases that triggered the process. When the buzzer sounded, he pulled his foil from his second startled grasp and ran forward. Our role looked amazed at the sudden fury of the attack, then smiled. He thought it was the last burst of energy. He knew how close they both were to exhaustion. Brienne saw something close to panic on his opponent's face when the man finally recognized his error. A wave of despair rolled out from our rogue. Brienne sensed it and knew the fifth point was his. In the powerful twist that's rest of the side, in and under the guard."] # fmt: on processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") model = model.to(torch_device) ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean") one_audio = np.concatenate([x["array"] for x in ds["validation"]["audio"]], dtype=np.float32) input_features = processor( one_audio, return_tensors="pt", truncation=False, padding="longest", sampling_rate=16_000 )["input_features"] input_features = input_features.to(device=torch_device) gen_kwargs = { "return_timestamps": True, "no_speech_threshold": 0.6, "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), "num_beams": 2, "compression_ratio_threshold": 1.35, "condition_on_prev_tokens": True, "logprob_threshold": -1.0, "renormalize_logits": True, # necessary to match OAI beam search implementation } torch.manual_seed(0) result = model.generate(input_features, **gen_kwargs) decoded = processor.batch_decode(result, skip_special_tokens=True) self.assertEqual(decoded, EXPECTED_TEXT) @slow def test_whisper_longform_multi_batch(self): # fmt: off EXPECTED_TEXT_1 = [" Mr. Quilter's manner less interesting than his matter. He tells us that at this festive season of the year, with Christmas and roast beef looming before us, similes drawn from eating and its results occur most readily to the mind. He has grave doubts whether Sir Frederick Layton's work is really Greek after all, and can discover in it but little of rocky Ithaca. Linnell's pictures are a sort of up-gards and atom paintings, and Mason's exquisite idles are as national as a jingo poem. Mr. Birkett Foster's landscapes smile at one much in the same way that Mr. Carker used to flash his teeth. And Mr. John Collier gives his sitter a cheerful slap in the back, before he says, like a shampooer and a Turkish bath. Next man, it is obviously unnecessary for us to point out how luminous these criticisms are, how delicate an expression. On the general principles of art, Mr. Quilter writes with equal lucidity. Painting he tells us is of a different quality to mathematics, and finish in art is adding more effect. As for etchings, there are two kinds, British and foreign. He laments most bitterly the divorce that has been made between decorative art and what we usually call pictures. Mix a customary appeal to the last judgment and reminds us that in the great days of art Michelangelo was the furnishing a poster or near the fire, and the ornaments Fred brought home from India on the mental board. In fact, he is quite severe on Mr. Ruskin for not recognizing that a picture should denote the frailty of man. And remarks was pleasing courtesy in Felicitis Grace that many faces are feeling. Only unfortunately his own work never does get good. Mr. Quilter has missed his chance, for he has failed even to make himself the Tupper of painting. a Harry Quilter M.A. Because you were sleeping instead of conquering, the lovely rose princess has become a fiddle without a bow, while poor Shaggy sits there, accooing dove. He has gone, and gone for good, answered Polychrome, who had managed to squeeze into the room beside the dragon, and had witnessed the occurrences with much interest. I have remained a prisoner only because I wished to be one. And with this, he stepped forward and burst the stout chains as easily as if they had been threads. The little girl had been asleep, but she heard the wraps and opened the door. The king has flooded disgrace, and your friends are asking for you. I begged Ruggadot a long ago to send him away, but he would not do so. I also offered to help your brother to escape, but he would not go. He eats and sleeps very steadily, replied the new king. I hope he doesn't work too hard, St. Shaggy. He doesn't work at all. In fact, there's nothing he can do in these dominions as well as our gnomes, whose numbers are so great that it worries us to keep them all busy. Not exactly, we've turned Calico. Where is my brother now, inquired Shaggy. In the metal forest. Where is that? The middle forest is in the great domed cavern, the largest and all-ard dominions, replied Calico. Calico hesitated. However, if we look sharp, we may be able to discover one of these secret ways. Oh no, I'm quite sure he didn't. That's funny, remarked Betsy thoughtfully. I don't believe Anne knew any magic, or she'd have worked it before. I do not know, confess Shaggy. True, agreed Calico. Calico went to the big gong and pounded on it, just as Virgato used to do, but no one answered the summons. Having returned to the Royal Cavern, Calico first pounded the gong and then sat in the throne, wearing Virgados discarded Ruby Crown and holding in his hand to scepter, which Virgato had so often thrown at his head. A man said to the universe, Sir, I exist. Sweat-covered Breon's body trickling into the tight-lowing cloth that was the only german he wore. The cut on his chest is still dripping blood. The ache of his overstrained eyes, even the soaring arena around him with thousands of spectators, retrovealities not worth thinking about. His instant panic was followed by a small sharp, blow high on his chest. One minute, a voice said, and a time buzzer sounded. A minute is not a very large measure of time, and his body needed every fraction of it. The buzzers were, triggered his muscles into complete relaxation. Oliya's heart and lungs worked on at a strong, measured rate. He was in reverie, sliding along the borders of consciousness. The contestants in the 20s needed undisturbed rest. Therefore, knights and the dormitories were as quiet as death. Particularly so, on this last night, when only two of the little cubicles were occupied, the thousands of others standing with dark empty doors. The other voice snapped with a harsh urgency clearly used to command. I'm here because the matter is of utmost importance, and brand is the one I must see. Now stand aside. the twenties, he must have drawn his gun, because the intruder said quickly, but that away you're being a fool. Out, there was silence then, and still wondering, Breon was once more asleep. Ten seconds, he asked the handler who was needing his aching muscles. A red-haired mountain of a man with an apparently inexhaustible store of energy. There could be little art in this last and final round of fencing. Just thrust and parry and victory to the stronger. Every man who entered the twenties had his own training tricks. There appeared to be an immediate association with the death trauma as if the two were inextricably linked into one. The strength that enables someone in a trance to hold his body stiff and unsupported except at two points, the head and heels. This is physically impossible when conscious. Others had died before during the twenties and death during the last round was, in some ways, easier than defeat. Breeding deeply, Breon's softly spoke the auto-hypnotic phrases that triggered the process. When the buzzer sounded, he pulled his foil from his second started grasp and ran forward. Our role had looked amazed at the sudden fury of the attack, then smiled. He thought it was the last burst of energy. He knew how close they both were to exhaustion. Breon saw something close to panic on his opponent's face when the man finally recognized his error. A wave of despair rolled out from our role. and sensed it and knew the fifth point was his. Then the powerful twist that's thrust to the side in and under the guard."] EXPECTED_TEXT_2 = [" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel. Nor is Mr. Quilter's manner less interesting than his matter. He tells us that at this festive season of the year, with Christmas and roast beef looming before us, similes drawn from eating and its results occur most readily to the mind. He has grave doubts whether Sir Frederick Layton's work is really Greek after all, and can discover in it but little of rocky Ithaca. Linnell's pictures are a sort of up-gards and atom paintings, and Mason's exquisite idles are as national as a jingo poem. Mr. Burkett Foster's landscapes smile at one much in the same way that Mr. Carker."] EXPECTED_TEXT_3 = [" possible. Nor is Mr. Quilter's manner less interesting than his matter. He tells us that at this festive season of the year, with Christmas and roast beef looming before us, similes drawn from eating and its results occur most readily to the mind. He has grieved doubts whether Sir Frederick Layton's work is really greek after all, and can discover in it but little of rocky Ithaca. Linnell's pictures are a sort of up-guards and atom paintings, and Mason's exquisite idles are as national as a jingo poem. Mr. Birk at Foster's landscapes smile at one much in the same way that Mr. Carker used to flash his teeth. And Mr. John Collier gives his sitter a cheerful slap in the back, before he says, like a shampooer and a Turkish bath, next man, it is obviously unnecessary for us to point out how luminous these criticisms are, how delicate an expression. Under general principles of art, Mr. Quilter writes with equal lucidity. Painting, he tells us, is of a different quality to mathematics and finish in art is adding more effect. As for etchings, there are two kinds, British and foreign. He laments most bitterly the divorce that has been made between decorative art and what we usually call pictures. Mix a customary appeal to the last judgment and reminds us that in the great days of art Michelangelo was the furnishing upholsterer. Near the fire. any ornaments Fred brought home from India on the mental board. In fact, he is quite severe on Mr. Ruskin for not recognizing that a picture should denote the frailty of man, and remarks was pleasing courtesy in Felicitis Grace that many faces are feeling. Only, unfortunately, his own work never does get good. Mr. Quilter has missed his chance, for he has failed even to make himself the tupper of painting. By Harry Quilter, M.A. Because he was sleeping instead of conquering, the lovely rose princess has become a fiddle without a bow, all poor ashaggy sits there, accoing dove. He has gone and gone for good, answered Polychrome, who had managed to squeeze into the room beside the dragon, and had witnessed the occurrences with much interest. I have remained a prisoner only because I wished to be one. And with this, he stepped forward and burst the stout chains as easily as if they had been threads. The little girl had been asleep, but she heard the wraps and opened the door. The king has fled and disgraced, and your friends are asking for you. I begged Ruggadot a long ago to send him away, but he would not do so. I also offered to help your brother to escape, but he would not go. He eats and sleeps very steadily, replied the new king. I hope he doesn't work too hard, St. Shaggy. He doesn't work at all. In fact, there's nothing he can do in these dominions as well as our gnomes, whose numbers are so great that it worries us to keep them all busy. Not exactly, we've turned Calico. Where is my brother now, inquired Shaggy. In the metal forest. Where is that? The middle forest is in the great domed cavern, the largest and all-ard dominions, replied Calico. Calico hesitated. However, if we look sharp, we may be able to discover one of these secret ways. Oh no, I'm quite sure he didn't. That's funny, remarked Betsy thoughtfully. I don't believe Anne knew any magic, or she'd have worked it before. I do not know, confess Shaggy. True, agreed Calico. Calico went to the big gong and pounded on it, just as Virgato used to do, but no one answered the summons. Having returned to the Royal Cavern, Calico first pounded the gong and then sat in the throne, wearing Virgados discarded Ruby Crown and holding in his hand the scepter, which Virgato had so often thrown at his head. The man said to the universe, Sir, I exist. Sweat-covered Breon's body trickling into the tight-lowing cloth that was the only german to war. The cut on his chest still dripping blood. The ache of his overstrained eyes, even to soaring arena around him with thousands of spectators, retroveilities not worth thinking about. His instant panic was followed by a small sharp, blow high on his chest. One minute, a voice said, and a time buzzer sounded. A minute is not a very large measure of time, and his body needed every fraction of it. The buzzers were triggered as muscles into complete relaxation. Oily his heart and lungs worked on at a strong, measured rate. He was in reverie, sliding along the borders of consciousness. The contestants in the 20s needed undisturbed rest. Therefore, knights and the dormitories were as quiet as death. Particularly so, on this last night, when only two of the little cubicles were occupied, the thousands of others standing with dark empty doors. The other voice snapped with a harsh urgency clearly used to command. I'm here because the matter is of utmost importance, and brand is the one I must see. Now stand aside. the twenties, he must have drawn his gun, because the intruder said quickly, but that away you're being a fool. Out, there was silence then, and still wondering, Breon was once more asleep. Ten seconds, he asked the handler who was needing his aching muscles. A red-haired mountain of a man, with an apparently inexhaustible store of energy. There could be little art in this last and final round of fencing. Just thrust and parry and victory to the stronger. Every man who entered the twenties had his own training tricks. There appeared to be an immediate association with the death trauma, as if the two were inextricably linked into one. The strength that enables someone in a trance to hold his body stiff and unsupported except at two points, the head and heels. This is physically impossible when conscious. Others had died before during the twenties and death during the last round was, in some ways, easier than defeat. Breeding deeply, Breon softly spoke the auto-hypnotic phrases that triggered the process. When the buzzer sounded, he pulled his foil from his second startled grasp and ran forward. Our role looked amazed at the sudden fury of the attack, then smiled. He thought it was the last burst of energy. He knew how close they both were to exhaustion. Breon saw something close to panic on his opponent's face when the man finally recognized his error. A wave of despair rolled out from our role. Breon sensed it and knew the fifth point was his. the powerful twist that's rest of the side, in and under the guard."] EXPECTED_TEXT_4 = [" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel. Nor is Mr. Quilter's manner less interesting than his matter. He tells us that at this festive season of the year, with Christmas and roast beef looming before us, similes drawn from eating and its results occur most readily to the mind. He has grave doubts whether Sir Frederick Layton's work is really Greek after all, and can discover in it but little of rocky Ithaca. Linnell's pictures are a sort of up-gards and atom paintings, and Mason's exquisite idles are as national as a jingo poem. Mr. Birk at Foster's landscapes smile at one much in the same way that Mr. Carker used to flash his teeth. Mr. John Collier gives his sitter a cheerful slap in the back, before he says, like a shampoo or a Turkish bath. Next man, it is obviously unnecessary for us to point out how luminous these criticisms are, how delicate an expression. On the general principles of art, Mr. Quilter writes with equal lucidity. he tells us is of a different quality to mathematics, and finish in art is adding more effect. As for etchings, there are two kinds, British and foreign. He laments most bitterly the divorce that has been made between decorative art and what we usually call pictures. Makes the customary appeal to the last judgment and reminds us that in the great days of art Michelangelo was the furnishing upholsterer. Near the fire, any ornaments Fred brought home from India on the mantelboard. In fact, he is quite severe on Mr. Ruskin for not recognizing that a picture should denote the frailty of man. And remarks was pleasing courtesy in Felicitis Grace that many faces are feeling. Only, unfortunately, his own work never does get good. Mr. Quilter has missed his chance, for he has failed even to make himself the Tupper of painting. By Harry Quilter M.A. Because you were sleeping instead of conquering, the lovely rose princess has become a fiddle without a bow, while poor Shaggy sits there, accoing dove. He has gone and gone for good, answered Polychrome, would manage to squeeze into the room beside the dragon and had witnessed the occurrences with much interest. I have remained a prisoner only because I wished to be one. And with this, he stepped forward and burst the stout chains as easily as if they had been threads. The little girl had been asleep, but she heard the wraps and opened the door. The king has fled and disgraced and your friends are asking for you. I begged Ruggadot long ago to send him away, but he would not do so. I also offered to help your brother to escape, but he would not go. He eats and sleeps very steadily, replied the new king. I hope he doesn't work too hard, since Shaggy. He doesn't work at all. In fact, there's nothing he can do in these dominions, as well as our gnomes, whose numbers are so great that it worries us to keep them all busy. Not exactly, we've turned Calico. Where is my brother now? In Quared Shaggy. In the metal forest. Where is that? The metal forest is in the great domed cavern, the largest and all-ard dominions, replied Calico. Calico hesitated. However, if we look sharp, we may be able to discover one of these secret ways. Oh no, I'm quite sure he didn't. That's funny, remarked Betsy thoughtfully. I don't believe and knew any magic or she'd have worked it before. I do not know, confess shaggy. True, a great calico. Calico went to the big gong and pounded on it just as we're good to use to do, but no one answered the summons. Having returned to the Royal Cavern, Calico first pounded the gong and then sat in the throne, wearing ruggedos discarded ruby crown and holding in his hand to scepter which ruggedo had so often thrown at his head. A man said to the universe, Sir, I exist. Sweat covered Breon's body, trickling into the titling cloth that was the only german he wore. The cut on his chest still dripping blood. The ache of his overstrained eyes, even the soaring arena around him with thousands of spectators, retrovealities not worth thinking about. His instant panic was followed by a small sharp blow high on his chest. One minute, a voice said, and a time buzzer sounded. A minute is not a very large measure of time, and his body needed every fraction of it. The buzzers were triggered as muscles into complete relaxation. Oli's heart and lungs worked on at a strong, measured rate. He was in reverie, sliding along the borders of consciousness. The contestants in the 20s needed undisturbed rest. Therefore, nights in the dormitories were as quiet as death. Particularly so, on this last night, when only two of the little cubicles were occupied, The thousands of others standing with dark empty doors. The other voice snapped with a harsh urgency, clearly used to command. I'm here because the matter is of utmost importance, and brand is the one I must see. Now stand aside. The twenties, he must have drawn his gun because the intruder said quickly, but that away you're being a fool. out, there was silence then, and still wondering, Breon was once more asleep. Ten seconds, he asked the handler who was needing his aching muscles. A red-haired mountain of a man, with an apparently inexhaustible store of energy. There could be little art in this last and final round of fencing. Just thrust and parry, and victory to the stronger. a man who entered the twenties had his own training tricks. They were appeared to be an immediate association with the death trauma, as if the two were inextricably linked into one. The strength that enables someone in a trance to hold his body stiff and unsupported except at two points, the head and heels. This is physically impossible when conscious. had died before during the 20s and death during the last round was in some ways easier than defeat. Breathing deeply, Breon's softly spoke the auto-hypnotic phrases that triggered the process. When the buzzer sounded, he pulled his foil from his second startled grasp and ran forward. Our role looked amazed at the sudden fury of the attack, then smiled. He thought it was the last burst of energy. He knew how close they both were to exhaustion. Breon saw something close to panic on his opponent's face when the man finally recognized his error. A wave of despair rolled out from our rogue. Breon sensed it and knew the fifth point was his. the powerful twist that's rest of the side, in and under the guard."] # fmt: on processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") model = model.to(torch_device) ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean") one_audio = np.concatenate([x["array"] for x in ds["validation"]["audio"]], dtype=np.float32) audios = [] audios.append(one_audio[110000:]) audios.append(one_audio[:800000]) audios.append(one_audio[80000:]) audios.append(one_audio[:]) decoded_single = [] for audio in audios: inputs = processor(audio, return_tensors="pt", truncation=False, sampling_rate=16_000) inputs = inputs.to(device=torch_device) result = model.generate(**inputs, return_timestamps=True) decoded_single.append(processor.batch_decode(result, skip_special_tokens=True)) inputs = processor( audios, return_tensors="pt", truncation=False, padding="longest", return_attention_mask=True, sampling_rate=16_000, ) inputs = inputs.to(device=torch_device) result = model.generate(**inputs, return_timestamps=True) decoded_all = processor.batch_decode(result, skip_special_tokens=True) # make sure single & batch is exactly the same self.assertEqual(decoded_all[0:1], decoded_single[0]) self.assertEqual(decoded_all[1:2], decoded_single[1]) self.assertEqual(decoded_all[2:3], decoded_single[2]) self.assertEqual(decoded_all[3:4], decoded_single[3]) # exact match self.assertEqual(decoded_all[0:1], EXPECTED_TEXT_1) self.assertEqual(decoded_all[1:2], EXPECTED_TEXT_2) self.assertEqual(decoded_all[2:3], EXPECTED_TEXT_3) self.assertEqual(decoded_all[3:4], EXPECTED_TEXT_4) @slow def test_whisper_longform_multi_batch_prev_cond(self): # fmt: off EXPECTED_TEXT_1 = [" Mr. Quilters manner less interesting than his matter. He tells us that at this festive season of the year, with Christmas and roast beef looming before us, similarly drawn from eating and its results occur most readily to the mind. He has grave doubts whether Sir Frederick Layton's work is really Greek after all and can discover in it but little of Rocky Ithaca. The Nils, pictures are sort of upguards and atom paintings and Mason's exquisite itals are as national as a jingo poem. Mr. Berkett Foster's landscapes smile at one much in the same way that Mr. Carker used to flash his teeth. And Mr. John Collier gives his sitter a cheerful slap on the back before he says like a shampooer and a Turkish bath. Next man, it is obviously unnecessary for us to point out how luminous these criticisms are, how delicate and expression. On the general principles of art, Mr. Quilters writes with equal lucidity. Painting he tells us is of a different quality to mathematics and finish in art is adding more effect. As for etchings, there are of two kinds, British and foreign. He laments most bitterly the divorce that has been made between decorative art and what we usually call pictures makes a customary appeal to the last judgment and reminds us that in the great days of art Michelangelo was the furnishing apostorer. Near the fire, any ornaments Fred brought home from India on the mental board. In fact, he is quite severe on Mr. Ruskin, for not recognizing that a picture should denote the frailty of man. And remarks with pleasing courtesy and solicitous grace that many phases of feeling only, unfortunately, his own work never does get good. Mr. Quilters has missed his chance, for he has failed even to make himself the tougher of painting. My hair equal to MA. Because he was sleeping instead of conquering, the lovely rose princess has become a fiddle with a bow, while poor shaggy sits there, a cooling dove. He has gone and gone for good, answered polychrome, who had managed to squeeze into the room beside the dragon and had witnessed the occurrences with much interest. I have remained a prisoner only because I wished to be one. And with this, he stepped forward and burst the stout chains as easily as if they had been threads. The little girl had been asleep, but she heard the wraps and opened the door. The king has fled in disgrace in your friends, they are asking for you. I begged Ruggedo long ago to send him away, but he would not do so. I also offered to help you brother to escape, but he would not go. He eats and sleeps very steadily, replied the new king. I hope he doesn't work too hard since shaggy. He doesn't work at all. In fact, there is nothing he can do in these dominions as well as our nooms, whose numbers are so great that it worries us to keep them all busy. Not exactly, we've turned Calico. Where is my brother now in Quarage Shaggy? In the metal forest. Where is that? The metal forest is in the great domed cavern. The largest and all our dominions replied Calico. Calico hesitated. However, if we look sharp, we may be able to discover one of these secret ways. Oh no, I'm quite sure he didn't. That's funny remarked but see you thoughtfully. I don't believe Anne knew any magic or she'd have worked it before. I do not know, confessed Shaggy. True, agreed Calico. Calico went to the big gong and pounded on it just as we're good to use to do, but no one answered the summons. Having returned to the royal cavern, Calico first pounded the gong and then sat in the throne, wearing reggos, discarded ruby crown, and holding in his hand to scepter which reggado had so often thrown at his head. The man said to the universe, Sir, I exist. Sweat covered Brianna's body trickling into the tight-wing cloth that was the only garment he wore. The cut on his chest still dripping blood. The ache of his overstrained eyes, even the soaring arena around him with thousands of spectators, retrievalidies not worth thinking about. His instant panic was followed by a small sharp blow high on his chest. One minute of voice said, and the time buzzer sounded. A minute is not a very large measure of time, and his body needed every fraction of it. The buzzer's were triggered as muscles into complete relaxation. Only his heart and lungs worked on at a strong, measured rate. He was in reverie sliding out on the borders of consciousness. The contestants in the twenties needed undisturbed rest. Therefore, knights and the dormitories were as quiet as death. Particularly so, on this last night, when only two of the little cubicles were occupied, the thousands of others standing with dark empty doors. The other voice snapped with a harsh urgency, clearly used to command. I'm here because the matter is of utmost importance, and brand is the one I must see. Now stand aside. But at the end of the 20s, he must have drawn his gun because the intruder said quickly, but that away, he'd be no fool. Out, the resoundance then, and still wondering, Brienne was once more asleep. Ten seconds, he asked the handler who was needing his aching muscles. A red-haired mountain of a man, with an apparently inexhaustible story of energy. There could be little art in this last and final round of fencing, just thrust and parry and victory to the stronger. Every man who entered the 20s had his own training tricks. There appeared to be an immediate association with the death trauma, as if the two were inexplicably linked into one. The strength that enables someone in a trance to hold his body stiff and unsupported, except at two points, the head and heels. This is physically impossible when conscious. Others had died before during the 20s, and death during the last round was, in some ways, easier than defeat. Breathing deeply, Brienne's softly spoke the autahypnotic phrases that triggered the process. When the buzzer sounded, he pulled his foil from his second startled grasp and ran forward. Her role clipped the maze at the sudden fury of the attack, then smiled. He thought it was the last burst of energy. He knew how closely both were to exhaustion. Brienne saw something close to panic on his opponent's face when the man finally recognized his error. A wave of despair rolled out from her role. Brienne sensed it and knew the fifth point was his. In the powerful twist that's first to decide. In and under the guard."] EXPECTED_TEXT_2 = [" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel. Nor is Mr. Quilter's manner less interesting than his matter. He tells us that at this festive season of the year, with Christmas and roast beef looming before us, similarly drawn from eating and its results occur most readily to the mind. He has grave doubts whether Sir Frederick Latins' work is really Greek after all, and can discover in it but little of rocky Ithaca. Lennials, pictures are a sort of upguards and atom paintings, and Mason's exquisite idles are as national as a jingo poem. Mr. Berkett Foster's landscapes smile at one much in the same way that Mr. Carker"] EXPECTED_TEXT_3 = [" gospel. Nor is Mr. Quilter's manner less interesting than his matter. He tells us that at this festive season of the year, with Christmas and roast beef looming before us, similarly drawn from eating in its results occur most readily to the mind. He has grave doubts whether Sir Frederick Latins work is really Greek after all and can discover in it but little of rocky ithaka. Lennils, pictures, are a sort of upguards and atom paintings and Mason's exquisite itals are as national as a jingo poem. Mr. Birkut Foster's landscapes smile at one much in the same way that Mr. Carker used to flash his teeth. And Mr. John Collier gives his sitter a cheerful slap on the back before he says like a shampooer and a Turkish bath. Next man, it is obviously unnecessary for us to point out how luminous these criticisms are, how delicate and expression. Under general principles of art, Mr. Quilter writes with equal lucidity. Painting he tells us is of a different quality to mathematics and finish in art is adding more effect. As for etchings, thereof two kinds, British and foreign. He laments most bitterly the divorce that has been made between decorative art and what we usually call pictures makes a customary appeal to the last judgment and reminds us that in the great days of art Michelangelo was the furnishing apostoror. Near the fire, any ornaments spread brought home from India on the mental board. In fact, he is quite severe on Mr. Ruskin for not recognizing that a picture should denote the frailty of man. And remarks with pleasing courtesy and solicitous grace that many faces are feeling, only unfortunately his own work never does get good. Mr. Quilter has missed his chance. For he has failed even to make himself the tougher of painting by Harry Quilter MA. Because he was sleeping instead of conquering, the lovely Rus princess has become a fiddle with a bow while poor shaggy sits there, a cooling dove. He has gone and gone for good. Answered polychrome, who had managed to squeeze into the room beside the dragon and had witnessed the occurrences with much interest. I have remained the prisoner only because I wished to be one. And with this, he stepped forward and burst the stout chains as easily as if they had been threads. The little girl had been asleep, but she heard the wraps and opened the door. The king has fled in disgrace in your friends, they are asking for you. I begged Ruggedo long ago to send him away, but he would not do so. I also offered to help your brother to escape, but he would not go. He eats and sleeps very steadily, replied the new king. I hope he doesn't work too hard, such a shaggy. He doesn't work at all. In fact, there is nothing he can do in these dominions as well as our nooms, whose numbers are so great that it worries us to keep them all busy. Not exactly, we've turned Calico. Where is my brother now, inquired Shaggy, in the metal forest? Where is that? The metal forest is in the great domed cavern, the largest and all our dominions replied Calico. Calico hesitated. However, if we look sharp, we may be able to discover one of these secret ways. Oh no, I'm quite sure he didn't. That's funny, remarked a bedsy thoughtfully. I don't believe Anne knew any magic or she'd have worked before. I do not know, confessed Shaggy. True, agreed Calico. Calico went to the big gong and pounded on it just as Ruggedo used to do, but no one answered the summons. Having returned to the royal cavern, Calico first pounded the gong and then sat in the throne, wearing Ruggedo's discarded ruby crown and holding in his hand the scepter which Ruggedo had so often thrown at his head. A man said to the universe, Sir, I exist. Sweat covered Breon's body, trickling into the tight-wing cloth that was the only garment he wore. The cut on his chest still dripping blood. The ache of his overstrain dyes, even the soaring arena around him with thousands of spectators, retrievalidates not worth thinking about. His instant panic was followed by a small sharp blow high on his chest. One minute, a voice said, and a time buzzer sounded. A minute is not a very large measure of time and his body needed every fraction of it. The buzzer's were triggered as muscles into complete relaxation. Only his heart and lungs worked on at a strong, measured rate. He was in reverie sliding out on the borders of consciousness. The contestants in the 20s needed undisturbed rest. Therefore, knights in the dormitories were as quiet as death. Particularly so, on this last night, when only two of the little cubicles were occupied, the thousands of others standing with dark empty doors. The other voice snapped with a harsh urgency, clearly used to command. I'm here because the matter is of utmost importance, and brand is the one I must see. Now stand aside. To 20s, he must have drawn his gun because the intruder said quickly, but that away, he'd be no fool. Out, there was silence then, and still wondering, Brienne was once more asleep. Ten seconds, he asked the handler who was needing his aching muscles. A red-haired mountain of a man, with an apparently inexhaustible story of energy. There could be little art in this last and final round of fencing, just thrust and parry and victory to the stronger. Every man who entered the 20s had his own training tricks. There appeared to be an immediate association with the death trauma as if the two were inexplicably linked into one. The strength that enables someone in a trance to hold his body stiff and unsupported, except at two points, the head and heels. This is physically impossible when conscious. Others had died before during the 20s, and death during the last round was, in some ways, easier than defeat. Breathing deeply, Brienne softly spoke the odd hypnotic phrases that triggered the process. When the buzzer sounded, he pulled his foil from his second startled grasp and ran forward. I rolled up the maze at the sudden fury of the attack, then smiled. He thought it was the last burst of energy. He knew how close they both were to exhaustion. Brienne saw something close to panic on his opponent's face when the man finally recognized his error. A wave of despair rolled out from our old. Brienne sensed it and knew it was a fifth point was his. Then the powerful twist that's for us to decide in and under the guard."] EXPECTED_TEXT_4 = [" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel. Nor is Mr. Quilter's manner less interesting than his matter. He tells us that at this festive season of the year, with Christmas and roast beef looming before us, similarly drawn from eating and its results occur most readily to the mind. He has grave doubts whether Sir Frederick Latins' work is really Greek after all, and can discover in it but little of rocky Ithaca. Lennils, pictures, are a sort of upguards and atom paintings, and Mason's exquisite idles are as national as a jingo poem. Mr. Berkett Foster's landscapes smile at one much in the same way that Mr. Carker used to flash his teeth. And Mr. John Collier gives his sitter a cheerful slap on the back before he says, like a shampooer in a Turkish bath. Next man, it is obviously unnecessary for us to point out how luminous these criticisms are, how delicate and expression. On the general principles of art, Mr. Quilter writes with equal lucidity. Painting he tells us is of a different quality to mathematics, and finish in art is adding more effect. As for etchings, thereof two kinds, British and foreign. He laments most bitterly the divorce that has been made between decorative art and what we usually call pictures makes a customary appeal to the last judgment and reminds us that in the great days of art Michelangelo was the furnishing apostorer. Near the fire, any ornaments Fred brought home from India on the mental board. In fact, he is quite severe on Mr. Ruskin, for not recognizing that a picture should denote the frailty of man. And remarks with pleasing courtesy and solicitous grace that many phases of feeling only, unfortunately, his own work never does, get good. Mr. Quilter has missed his chance, for he has failed even to make himself the tougher of painting. My Harry Quilter, MA. Because he was sleeping instead of conquering, the lovely rose princess has become a fiddle with a bow, while poor shaggy sits there, a cooling dove. He has gone and gone for good, answered polychrome, who had managed to squeeze into the room beside the dragon, and had witnessed the occurrences with much interest. I have remained a prisoner only because I wished to be one. And with this, he stepped forward and burst the stout chains as easily as if they had been threads. The little girl had been asleep, but she heard the wraps and opened the door. The king has fled in disgrace in your friends, they are asking for you. I begged Ruggedo a long ago to send him away, but he would not do so. I also offered to help your brother to escape, but he would not go. He eats and sleeps very steadily, replied the new king. I hope he does not work too hard, since Shaggy. He doesn't work at all. In fact, there is nothing he can do in these dominions, as well as our nooms, whose numbers are so great that it worries us to keep them all busy. Not exactly, we've turned Calico, whereas my brother now, in Quilter Shaggy, in the metal forest. Where is that? The metal forest is in the great domed cavern, the largest and all our dominions replied Calico. Calico hesitated. However, if we look sharp, we may be able to discover one of these secret ways. Oh no, I'm quite sure he didn't. That's funny, remarked a bit, see you thoughtfully. I don't believe Anne knew any magic, or she'd have worked it before. I do not know, confessed Shaggy. True, agreed Calico. Calico went to the big gong and pounded on it, just as we're good to have used to do, but no one answered the summons. Having returned to the royal cavern, Calico first pounded the gong and then sat in the throne, wearing reggos, discarded ruby crown, and holding in his hand to scepter which reggado had so often thrown at his head. A man said to the universe, Sir, I exist. Sweat covered Breon's body, trickling into the titling cloth of a zeal-neighurment he wore. The cut on his chest still dripping blood. The ache of his overstrained eyes, even the soaring arena around him with thousands of spectators, retrievalidies not worth thinking about. His instant panic was followed by a small sharp blow high on his chest. One minute, a voice said, and a time buzzer sounded. A minute is not a very large measure of time, and his body needed every fraction of it. The buzzer's were triggered as muscles into complete relaxation. Only his heart and lungs worked on at a strong, measured rate. He was in reverie, sliding out on the borders of consciousness. The contestants in the twenties needed undisturbed rest. Therefore, knights and the dormitories were as quiet as death. Particularly so, on this last night, when only two of the little cubicles were occupied, the thousands of others standing with dark empty doors. The other voice snapped with a harsh urgency, clearly used to command. I'm here because the matter is of utmost importance, and brand is the one I must see, and I'll stand aside. To twenties, he must have drawn his gun because the intruders had quickly, but that away, here being a fool. Out, there is silence then, and still wondering, Brian was once more asleep. Ten seconds, he asked the handler who was needing his aching muscles. I've read here at Mountain of a Man, with an apparently inexhaustible story of energy. There could be little art in this last and final round of fencing, just thrust and parry and victory to the stronger. Every man who entered the twenties had his own training tricks. There appeared to be an immediate association with the death trauma, as if the two were inexplicably linked into one. The strength that enables someone in a trance to hold his body stiff and unsupported, except at two points, the head and heels. This is physically impossible when conscious. Others had died before during the twenties, and death during the last round was, in some ways, easier than defeat. Breathing deeply, Brian's softly spoke the autahypnotic phrases that triggered the process. When the buzzer sounded, he pulled his foil from his second startled grasp and ran forward. I rolled the maze at the sudden fury of the attack, then smiled. He thought it was the last burst of energy. He knew how close they both were to exhaustion. Brian saw something close to panic on his opponent's face when the man finally recognized his error. A wave of despair rolled out from Irohog. Brian sensed it and knew the fifth point was his. In the powerful twist that's first to decide. In and under the guard."] # fmt: on processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") model = model.to(torch_device) ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean") one_audio = np.concatenate([x["array"] for x in ds["validation"]["audio"]], dtype=np.float32) audios = [] audios.append(one_audio[110000:]) audios.append(one_audio[:800000]) audios.append(one_audio[80000:]) audios.append(one_audio[:]) gen_kwargs = { "return_timestamps": True, "no_speech_threshold": 0.6, "temperature": 0.0, "compression_ratio_threshold": 1.35, "condition_on_prev_tokens": True, "logprob_threshold": -1.0, } decoded_single = [] for audio in audios: inputs = processor(audio, return_tensors="pt", truncation=False, sampling_rate=16_000) inputs = inputs.to(device=torch_device) result = model.generate(**inputs, **gen_kwargs) decoded_single.append(processor.batch_decode(result, skip_special_tokens=True)) # exact match self.assertEqual(decoded_single[0], EXPECTED_TEXT_1) self.assertEqual(decoded_single[1], EXPECTED_TEXT_2) self.assertEqual(decoded_single[2], EXPECTED_TEXT_3) self.assertEqual(decoded_single[3], EXPECTED_TEXT_4) @slow def test_whisper_longform_multi_batch_hard(self): # fmt: off EXPECTED_XPU = [ " Folks, if you watch the show, you know, I spent a lot of time right over there. Patiently and astutely scrutinizing the boxwood and mahogany chest set of the day's biggest stories developing the central headline pawns, definitely maneuvering an oso topical night to F6, fainting of classics, Sicilian, nade door variation on the news, all the while seeing eight moves deep and patiently marshalling the latest press releases into a fisher's shows in Lipnitsky attack that culminates in the elegant lethal slow-played It's an all-passant checkmate that is my nightly monologue, but sometimes sometimes folks, I... APPLAUSE Sometimes I... Startle away, cubside down in the monkey bars of a condemned playground on a super fun site. Get all hept up on goofballs, rummage that were discarded tag bag of defective toys. Yank out a fist bowl of disembodied doll limbs, toss them on a stained kid's place mat from a defunct denny's, set up a table inside a rusty cargo container down by the wharf and challenged toothless drifters to the godless, bug-house blitz of tournament that is my segment. Meanwhile!", " Folks, I spend a lot of time right over there, night after night after night, actually. Carefully selecting for you the day's noosiest, most aerodynamic headlines, stress testing, and those topical anti-lock breaks and power steering, painstakingly stitching, leather seating, so soft, it would make JD power and her associates blush to create the luxury sedan that is my nightly monologue. But sometimes, you sometimes, folks, I lurched a consciousness in the back of an abandoned school bus and slap myself awake with a crusty floor mat. Before using a mouse-bitten timing belt to strap some old plywood to a couple of discarded oil drums, then by the light of a heathen moon, render a gas tank out of an empty big gulp, fill with white claw and denatured alcohol, then light a match and let her rip and the demented one man soapboxed her be of news that is my segment. We need one!", " Ladies and gentlemen, you know, I spent a lot of time right over there Raising the finest Holstein news cattle firmly yet tenderly milking the latest headlines from their jokes swollen teats Churning the daily stories into the decadent proven-style style triple cream breed that is my nightly monologue But sometimes sometimes folks I stagger home hungry after being released by the police and Root around in the neighbor's trash can for an old milk carton scrape out the blooming dairy residue into the remains of a wet cheese rod I won from a rat in a pre-donned street fight. Put it in a discarded paint can to leave it to ferment next to a trash fire then hunker down and hallucinate while eating the listeria laden demon custard of news that is my segment. You mean one of them.", " Folks, if you watch this show, you know I spend most of my time right over there carefully sorting through the day's biggest stories and selecting only the most subtle and unblemished ostrich and crocodile news leather, which I then entrust to artisan graduates of the Ichol Gregoire Ferrandi, who carefully dye them in a palette of bright zesty shades and adorn them in the finest and most topical inlay work using hand tools and double magnifying glasses, then assemble them according to now classic and elegant geometry using our signature saddles stitching. In line it with bees, wax coated linen, finely attached a mallet hammer strap, pearl hardware, and close shed to create for you the one of a kind hoke couture, Erme's Birkin bag that is my monologue, but sometimes, sometimes folks, sometimes. Sometimes I wake up in the last car of an abandoned roller coaster at Coney Island where I'm I'm hiding from the triads. I have some engine lubricants out of a safe way bag and stagger down the shore to tear the sail off a beach schooner. Then I rip the coaxial cable out of an RV and elderly couple from Utah, Hank, and Mabel lovely folks. And use it to stitch the sail into a loose pouch like a rock sack. And I stow away in the back of a garbage truck to the junkyard where I pick through to the debris for only the broken toys that make me the saddest until I have loaded for you. The Hobo Fugitives bug out, bindle of news that is my segment. Me one!", " You know, folks, I spent a lot of time crafting for you a bespoke playlist of the day's biggest stories right over there. Meticulously selecting the most topical chakra affirming scented candles, and using Feng Shui to perfectly align the joke energy in the exclusive boutique yoga retreat that is my monologue. But sometimes just sometimes I go to the dumpster behind the waffle house at three in the morning, take off my shirt, cover myself, and used fry oil, wrap my hands with some double-duct tape by stole from the broken car window. Pound a six-pack of blueberry hard-seltzer and a sack of pills I stole from a parked ambulance. Then arm wrestle a raccoon in the back alley vision quest of news that is my segment. Meanwhile!", " You know, folks, I spend most of my time right over there. Mining the day's biggest, most important stories, collecting the finest, most topical iron or hand hammering it into joke panels. Then I craft sheets of bronze and blazing with patterns that tell an epic tale of conquest and glory. Then, using the Germanic tradition press black process, I place thin sheets of foil against the scenes and by hammering or otherwise applying pressure from the back, I project these scenes into a pair of cheat cards in a faceplate and finally using fluted strips of white alloyed molding, I divide the designs into framed panels and hold it all together using bronze rivets to create the beautiful and intimidating, Anglo-Saxon battle helm that is my nightly monologue. Sometimes, sometimes folks. Sometimes, just sometimes, I come into my sense as fully naked on the deck of a pirate-be-seag'd, melee container ship that picked me up floating on the detached door of a portapotty in the Indian Ocean. Then after a sun stroke-induced realization of the crew of this ship plans to sell me an exchange for a bag of oranges to fight off scurvy, I lead a mutiny using only a PVC pipe at a pool chain that accepting my new role as Captain and declaring myself king of the windarc seas. I grab a dirty mop bucket covered in barnacles and adorn it with the teeth of the vanquished to create the sopping wet pirate crown of news that is my segment. Meanwhile!", " Folks, if you watch this show, you know I spend most of my time right over there carefully blending for you the day's Newsiest most topical flower eggs milk and butter and Stranding into a fine batter to make delicate and informative comedy pancakes Then I glaze them in the juice and zest of the most relevant midnight Valencia oranges and douse it all and a fine Dela main de voyage cognac Before from bang and basting them tables. I deserve for you the James Beard award worthy crepe suzzette That is my nightly monologue, but sometimes just sometimes folks. I wake up in the baggage hold of Greyhound bus. It's being hoisted by the scrap yard claw toward the burn pit. Escape to a nearby abandoned price chopper where I scrounge for old bread scraps and busted open bags of starfruit candies and expired eggs. Chuck it all on a dirty hubcap and slap it over a tire fire before using the legs of a strain, pair of sweatpants and as oven mitts to extract and serve the demented transience poundcake of news that is my segment. Me, Guadalupe!", ' Folks, if you watched the show and I hope you do, I spent a lot of time right over there. Tiredlessly studying the lineage of the days most important thoroughbred stories and wholesome or headlines, working with the best trainers, money can buy to rear their comedy offspring with a hand that is stern yet gentle into the triple crown winning equine specimen. That is my nightly monologue, but sometimes, sometimes, folks, I break into an unincorporated veterinary genetics lab, and grab whatever test tubes I can find, and then under a grow light I got from a discarded chia pet. I mixed the pilfer DNA of a horse and whatever was in a tube labeled Keith Colan extra. Slurrying the concoction with caffeine pills and a microwave red bull, I screamed, sing a prayer to Janice, initiator of human life and God of transformation as a half horse, half man, freak. Seasons to life before me and the hideous collection of loose animal parts and corrupted man tissue that is my segment. Meanwhile!' ] EXPECTED_CUDA = [ " Folks, if you watch the show, you know, I spent a lot of time right over there. Patiently and astutely scrutinizing the boxwood and mahogany chest set of the day's biggest stories developing the central headline pawns, definitely maneuvering an oso topical night to F6, fainting a classic Sicilian, nade door variation on the news, all the while seeing eight moves deep and patiently marshalling the latest press releases into a fisher's shows in Lip Nitsky attack that culminates in the elegant lethal slow-played, all-passant checkmate that is my nightly monologue. But sometimes, sometimes, folks, I. CHEERING AND APPLAUSE Sometimes I startle away, cubside down in the monkey bars of a condemned playground on a super fun site. Get all hept up on goofballs. Rummage that were discarded tag bag of defective toys. Yank out a fist bowl of disembodied doll limbs, toss them on a stained kid's place mat from a defunct dennies. set up a table inside a rusty cargo container down by the Wharf and challenged toothless drifters to the godless bughouse blitz of tournament that is my segment. Meanwhile.", " Folks, I spend a lot of time right over there, night after night after night, actually. Carefully selecting for you the day's noosiest, most aerodynamic headlines, stress testing, and those topical anti-lock breaks and power steering, painstakingly stitching, leather seating so soft, it would make JD power and her associates blush to create the luxury sedan that is my nightly monologue. But sometimes, you sometimes, folks. I lurched a consciousness in the back of an abandoned school and slap myself awake with a crusty floor mat. Before using a mouse-bitten timing belt to strap some old plywood to a couple of discarded oil drums, then by the light of a heathen moon, render a gas tank out of an empty big gulp, fill with white claw and denatured alcohol, then light a match and let her rip and the demented one man soapbox derby of news that is my segment. Me, Guadalupe! No!", " Ladies and gentlemen, you know, I spent a lot of time right over there Raising the finest Holstein news cattle firmly yet tenderly milking the latest headlines from their jokes swollen teats Churning the daily stories into the decadent proven-style style triple cream breed that is my nightly monologue But sometimes sometimes folks I stagger home hungry after being released by the police and Root around in the neighbor's trash can for an old milk carton scrape out the blooming dairy residue into the remains of a wet cheese rod I won from a rat in a pre-donned street fight. Put it in a discarded paint can to leave it to ferment next to a trash fire then hunker down and hallucinate while eating the listeria laden demon custard of news that is my segment. You mean one of them.", " Folks, if you watch this show, you know I spend most of my time right over there carefully sorting through the day's biggest stories and selecting only the most subtle and unblemished ostrich and crocodile news leather, which I then entrust to artisan graduates of the Ichol Gregoire Ferrandi, who carefully dye them in a palette of bright zesty shades and adorn them in the finest and most topical inlay work using hand tools and double magnifying glasses, then assemble them according to now classic and elegant geometry using our signature saddles stitching. In line it with bees, wax, coated linen, finely attached a mallet, hammered strap, pearled hardware, and close-shit to create for you the one-of-a-kind hoke couture, Erme's Birkin bag that is my monologue. But sometimes, sometimes folks, sometimes. Sometimes I wake up in the last car of an abandoned roller coaster at Coney Island where I'm I'm hiding from the triads. I have some engine lubricants out of a safe way bag and stagger down the shore to tear the sail off a beach schooner. Then I rip the coaxial cable out of an RV and elderly couple from Utah, Hank, and Mabel lovely folks. And use it to stitch the sail into a loose pouch like a rock sack. And I stow away in the back of a garbage truck to the junkyard where I pick through to the debris for only the broken toys that make me the saddest until I have loaded for you. The Hobo Fugitives bug out, bindle of news that is my segment. Me one!", " You know, folks, I spent a lot of time crafting for you a bespoke playlist of the day's biggest stories right over there. Meticulously selecting the most topical chakra affirming scented candles, and using Feng Shui to perfectly align the joke energy in the exclusive boutique yoga retreat that is my monologue. But sometimes just sometimes I go to the dumpster behind the waffle house at three in the morning, take off my shirt, cover myself, and used fry oil, wrap my hands with some double-duct tape by stole from the broken car window. Pound a six-pack of blueberry hard-seltzer and a sack of pills I stole from a parked ambulance. Then arm wrestle a raccoon in the back alley vision quest of news that is my segment. Meanwhile!", " You know, folks, I spend most of my time right over there. Mining the day's biggest, most important stories, collecting the finest, most topical iron or hand hammering it into joke panels. Then I craft sheets of bronze and blazing with patterns that tell an epic tale of conquest and glory. Then, using the Germanic tradition press-black process, I place thin sheets of foil against the scenes and by hammering or otherwise applying pressure from the back, I project these scenes into a pair of cheat cards in a faceplate and, finally, using fluted strips of white alloyed molding, I divide the designs into framed panels and hold it all together using bronze rivets to create the beautiful and intimidating, Anglo-Saxon battle helm that is my nightly monologue. Sometimes, sometimes folks. Sometimes, just sometimes, I come into my sense as fully naked on the deck of a pirate besieged melee container ship that picked me up floating on the detached door of a portapotty in the Indian Ocean. Then after a sunstroke-induced realization of the crew of this ship plans to sell me an exchange for a bag of oranges to fight off scurvy, I lead a mutiny using only a PVC pipe at a pool chain that accepting my new role as Captain and declaring myself king of the windarc seas. I grab a dirty mop bucket covered in barnacles and adorn it with the teeth of the vanquished to create the sopping wet pirate crown of news that is my segment. Meanwhile!", " Folks, if you watch this show, you know I spend most of my time right over there carefully blending for you the day's Newsiest most topical flower eggs milk and butter and Stranding into a fine batter to make delicate and informative comedy pancakes Then I glaze them in the juice and zest of the most relevant midnight Valencia oranges and douse it all and a fine Dela main de voyage cognac Before prom baying and basting them tables. I deserve for you the James Beard award worthy crepe suzzette That is my nightly monologue, but sometimes just sometimes folks. I wake up in the baggage hold of Greyhound bus. It's being hoisted by the scrap yard claw toward the burn pit. Escape to a nearby abandoned price chopper where I scrounge for old bread scraps and busted open bags of starfruit candies and expired eggs. Chuck it all on a dirty hubcap and slap it over a tire fire before using the legs of a strain, pair of sweatpants and as oven mitts to extract and serve the demented transience poundcake of news that is my segment. Me, Guadalupe!", " Folks, if you watched the show and I hope you do, I spent a lot of time right over there. Tiredlessly studying the lineage of the days most important thoroughbred stories and whole-stiner headlines, working with the best trainers, money can buy to rear their comedy offspring with a hand that is stern yet gentle into the triple crown winning equine specimen. That is my nightly monologue, but sometimes, sometimes, folks, I break into an unincorporated veterinary genetics lab and grab whatever test tubes I can find and then under a grow light I got from a discarded chia pet. I mixed the pilfered DNA of a horse and whatever was in a tube labeled Keith Colan extra. Slurrying the concoction with caffeine pills and a microwave red bull, I screamed, sang a prayer to Janice, initiator of human life and God of transformation as a half horse, half man, freak. Seizes to life before me and the hideous collection of loose animal parts and corrupted man tissue that is my segment. Meanwhile!" ] EXPECTED_ROCM = [ " Folks, if you watch the show, you know, I spent a lot of time right over there. Patiently and astutely scrutinizing the boxwood and mahogany chest set of the day's biggest stories developing the central headline pawns, definitely maneuvering an oso topical night to F6, fainting of classics, Sicilian, nade door variation on the news, all the while seeing eight moves deep and patiently marshalling the latest press releases into a fisher's shows in Lipnitsky attack that culminates in the elegant lethal slow-played It's an all-passant checkmate that is my nightly monologue, but sometimes sometimes folks, I... APPLAUSE Sometimes I... Startle away, cubside down in the monkey bars of a condemned playground on a super fun site. Get all hept up on goofballs, rummage that were discarded tag bag of defective toys. Yank out a fist bowl of disembodied doll limbs, toss them on a stained kid's place mat from a defunct denny's, set up a table inside a rusty cargo container down by the wharf and challenged toothless drifters to the godless, bug-house blitz of tournament that is my segment. Meanwhile!", " Folks, I spend a lot of time right over there, night after night after night, actually. Carefully selecting for you the day's noosiest, most aerodynamic headlines, stress testing, and those topical anti-lock breaks and power steering, painstakingly stitching, leather seating, so soft, it would make JD power and her associates blush to create the luxury sedan that is my nightly monologue. But sometimes, you sometimes, folks, I lurched a consciousness in the back of an abandoned school bus and slap myself awake with a crusty floor mat. Before using a mouse-bitten timing belt to strap some old plywood to a couple of discarded oil drums, then by the light of a heathen moon, render a gas tank out of an empty big gulp, fill with white claw and denatured alcohol, then light a match and let her rip and the demented one man soapboxed her be of news that is my segment. We need one!", " Ladies and gentlemen, you know, I spent a lot of time right over there Raising the finest Holstein news cattle firmly yet tenderly milking the latest headlines from their jokes swollen teats Churning the daily stories into the decadent proven-style style triple cream breed that is my nightly monologue But sometimes sometimes folks I stagger home hungry after being released by the police and Root around in the neighbor's trash can for an old milk carton scrape out the blooming dairy residue into the remains of a wet cheese rod I won from a rat in a pre-donned street fight. Put it in a discarded paint can to leave it to ferment next to a trash fire then hunker down and hallucinate while eating the listeria laden demon custard of news that is my segment. You mean one of them.", " Folks, if you watch this show, you know I spend most of my time right over there carefully sorting through the day's biggest stories and selecting only the most subtle and unblemished ostrich and crocodile news leather, which I then entrust to artisan graduates of the Ichol Gregoire Ferrandi, who carefully dye them in a palette of bright zesty shades and adorn them in the finest and most topical inlay work using hand tools and double magnifying glasses, then assemble them according to now classic and elegant geometry using our signature saddles stitching. In line it with bees, wax coated linen, finely attached a mallet hammer strap, pearl hardware, and close shed to create for you the one of a kind hoke couture, Erme's Birkin bag that is my monologue, but sometimes, sometimes folks, sometimes. Sometimes I wake up in the last car of an abandoned roller coaster at Coney Island where I'm I'm hiding from the triads. I have some engine lubricants out of a safe way bag and stagger down the shore to tear the sail off a beach schooner. Then I rip the coaxial cable out of an RV and elderly couple from Utah, Hank, and Mabel lovely folks. And use it to stitch the sail into a loose pouch like a rock sack. And I stow away in the back of a garbage truck to the junkyard where I pick through to the debris for only the broken toys that make me the saddest until I have loaded for you. The Hobo Fugitives bug out, bindle of news that is my segment. Me one!", " You know, folks, I spent a lot of time crafting for you a bespoke playlist of the day's biggest stories right over there. Meticulously selecting the most topical chakra affirming scented candles, and using Feng Shui to perfectly align the joke energy in the exclusive boutique yoga retreat that is my monologue. But sometimes just sometimes I go to the dumpster behind the waffle house at three in the morning, take off my shirt, cover myself, and used fry oil, wrap my hands with some double-duct tape by stole from the broken car window. Pound a six-pack of blueberry hard-seltzer and a sack of pills I stole from a parked ambulance. Then arm wrestle a raccoon in the back alley vision quest of news that is my segment. Meanwhile!", " You know, folks, I spend most of my time right over there. Mining the day's biggest, most important stories, collecting the finest, most topical iron or hand hammering it into joke panels. Then I craft sheets of bronze and blazing with patterns that tell an epic tale of conquest and glory. Then, using the Germanic tradition press black process, I place thin sheets of foil against the scenes and by hammering or otherwise applying pressure from the back, I project these scenes into a pair of cheat cards in a faceplate and finally using fluted strips of white alloyed molding, I divide the designs into framed panels and hold it all together using bronze rivets to create the beautiful and intimidating, Anglo-Saxon battle helm that is my nightly monologue. Sometimes, sometimes folks. Sometimes, just sometimes, I come into my sense as fully naked on the deck of a pirate-be-seag'd, melee container ship that picked me up floating on the detached door of a portapotty in the Indian Ocean. Then after a sun stroke-induced realization of the crew of this ship plans to sell me an exchange for a bag of oranges to fight off scurvy, I lead a mutiny using only a PVC pipe at a pool chain that accepting my new role as Captain and declaring myself king of the windarc seas. I grab a dirty mop bucket covered in barnacles and adorn it with the teeth of the vanquished to create the sopping wet pirate crown of news that is my segment. Meanwhile!", " Folks, if you watch this show, you know I spend most of my time right over there carefully blending for you the day's Newsiest most topical flower eggs milk and butter and Stranding into a fine batter to make delicate and informative comedy pancakes Then I glaze them in the juice and zest of the most relevant midnight Valencia oranges and douse it all and a fine Dela main de voyage cognac Before from bang and basting them tables. I deserve for you the James Beard award worthy crepe suzzette That is my nightly monologue, but sometimes just sometimes folks. I wake up in the baggage hold of Greyhound bus. It's being hoisted by the scrap yard claw toward the burn pit. Escape to a nearby abandoned price chopper where I scrounge for old bread scraps and busted open bags of starfruit candies and expired eggs. Chuck it all on a dirty hubcap and slap it over a tire fire before using the legs of a strain, pair of sweatpants and as oven mitts to extract and serve the demented transience poundcake of news that is my segment. Me, Guadalupe!", " Folks, if you watched the show and I hope you do, I spent a lot of time right over there. Tiredlessly studying the lineage of the days most important thoroughbred stories and wholesome or headlines, working with the best trainers, money can buy to rear their comedy offspring with a hand that is stern yet gentle into the triple crown winning equine specimen. That is my nightly monologue, but sometimes, sometimes, folks, I break into an unincorporated veterinary genetics lab, and grab whatever test tubes I can find, and then under a grow light I got from a discarded chia pet. I mixed the pilfer DNA of a horse and whatever was in a tube labeled Keith Colan extra. Slurrying the concoction with caffeine pills and a microwave red bull, I screamed, sing a prayer to Janice, initiator of human life and God of transformation as a half horse, half man, freak. Seasons to life before me and the hideous collection of loose animal parts and corrupted man tissue that is my segment. Meanwhile!", ] # fmt: on expected_output = Expectations( {("xpu", None): EXPECTED_XPU, ("cuda", None): EXPECTED_CUDA, ("rocm", (9, 4)): EXPECTED_ROCM} ).get_expectation() processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") model = model.to(torch_device) ds = load_dataset("distil-whisper/meanwhile", "default")["test"] ds = ds.cast_column("audio", Audio(sampling_rate=16000)) num_samples = 8 audio = ds[:num_samples]["audio"] audios = [x["array"] for x in audio] gen_kwargs = { "return_timestamps": True, "no_speech_threshold": 0.6, # necessary to trigger no speech detection "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), "compression_ratio_threshold": 1.35, "condition_on_prev_tokens": False, "logprob_threshold": -2.0, # necessary to avoid triggering temp fallback that will introduce randomness since we are comparing to openai EXTECTED_TEXT } decoded_single = [] for audio in audios: inputs = processor(audio, return_tensors="pt", truncation=False, sampling_rate=16_000) inputs = inputs.to(device=torch_device) result = model.generate(**inputs, **gen_kwargs) decoded_single += processor.batch_decode(result, skip_special_tokens=True) inputs = processor( audios, return_tensors="pt", truncation=False, padding="longest", return_attention_mask=True, sampling_rate=16_000, ) inputs = inputs.to(device=torch_device) result = model.generate(**inputs, return_timestamps=True) decoded_all = processor.batch_decode(result, skip_special_tokens=True) self.assertListEqual(decoded_all, decoded_single) self.assertListEqual(decoded_all, expected_output) @slow def test_whisper_longform_multi_batch_hard_prev_cond(self): # fmt: off EXPECTED_TEXT = [ " Folks, if you watch the show, you know I spent a lot of time right over there. Patiently and astutely scrutinizing the boxwood and mahogany chest set of the day's biggest stories, developing the central headline pawns, definitely maneuvering an oh-so-topical night to F6, faming of classic Sicilian, named or variation on the news, all the while seeing eight moves deep and patiently marshalling the latest press releases into a fisher shows in lip-nitsky attack that culminates in the elegant lethal slow-played all-pass on checkmate that is my nightly monologue, but sometimes sometimes, sometimes folks I sometimes I start a little wake-up side down in the monkey bars of a condemned play ground on a super fun site, get all hept up on goofballs, rummage that would discard a tag bag of defective toys, yank out a fistball of disembodied doll limbs, toss them on a stain kid's place mad from a defunct denies, set up a table inside a rusty cargo container down by the warf and challenge toothless drifters to the godless bughouse blitz of tournament that is my segment. Meanwhile!", " Folks, I spent a lot of time right over there night after night, actually. Carefully selecting for you the day's newsiest, most aerodynamic headlines, stress testing on those topical anti-lock breaks and power steering, painstakingly stitching, leather-seeding, so soft, it would make JD power and her associates blush. To create the luxury sedan that is my nightly monologue, but sometimes I'm just sometimes folks. I lurched to consciousness in the back of an abandoned school bus and slapped myself awake with a crusty floor mat. Before using a mouse-bitten timing belt to strap some old plywood to a couple of discarded oil drums, then by the light of a heathen-moon render a gas tank out of an empty big gulp, filled with white claw and de-natured alcohol, then light a match, and letter-rip, and the dis-mented one-man, soapbox derby of news that is my segment. Meanwhile.", " Ladies and gentlemen, you know, I spent a lot of time right over there, raising the finest hosting news cattle firmly, yet tenderly milking the latest headlines from their jokes, swollen teats, churning the daily stories into the decadent Provincil style triple cream-breed. It is my nightly monologue, but sometimes sometimes I stagger home hungry after being released by the police and root around in the neighbor's trash can for an old milk carton scraped out the blooming dairy residue into the remains of a wet cheese rod I won from a rat in a pre-dawn street fight. Put it in a discarded paint can to leave it to ferment next to a trash fire than a hunker down in hallucinate while eating the Listeria latent demon custard of news that is my segment.", " Folks, you watched this show, you know I spend most of my time right over there, carefully sorting through the days, big stories, and selecting only the most subtle and unblemished ostrich and crocodile news leather, which I then entrust to artisan graduates of the Ickel Greg Waferandi, who carefully died them in a pallet of bright, zesty shades, and adorn them in the finest most topical inlay work, using hand tools and double magnifying glasses, then assemble them according to now classic and elegant geometry using our signature saddle stitching, and line it with bees, wax, coated linen, and finally attach a mallet hammered strap, purled hardware, and close-shet to create for you the one of a kind hot couture, earn-may's burkin bag that is my monologue, but sometimes, sometimes folks, sometimes. Sometimes I wake up in the last car of an abandoned roller coaster at Coney Island where I'm hiding from the triads, I huff some engine lubricants out of a safe way bag and staggered down the shore to tear the sail off a beach skoener, then I ripped the coaxial cable out of an RV and elderly couple from Utah, Hank, and Mabel, lovely folks, and use it to stitch the sail into a loose pouch-like rock sack, and I stow in the back of a garbage truck to the junkyard, where I pick through to the debris for only the broken toys that make me the saddest, until I have loaded for you the hobo fugitives bug out bindle of news that is my segment.", " You know, folks, I spent a lot of time crafting for you a bespoke playlist of the day's big stories right over there. meticulously selecting the most topical chakra affirming scented candles, using Feng Shui, to perfectly align the joke energy in the exclusive boutique yoga retreat that is my monologue, but sometimes just sometimes, I go to the dumpster behind the waffle house at three in the morning, take off my shirt, cover myself and use fry oil, wrap my hands and some old duct tape I stole from a broken car window, pound a six pack of blueberry hard-seller and a second pill, as I stole from a parked ambulance, then arm wrestle a raccoon in the back alley vision quest of news that is my segment.", ' You know, folks, I spend most of my time right over there. Mining the days, biggest, most important stories, collecting the finest, most topical iron or hand hammering it into joke panels, then I craft sheets of bronze and blazing with patterns that tell an epic tale of conquest and glory. Then, using the Germanic tradition press, black process, I place thin sheets of foil against the scenes and by hammering or otherwise applying pressure from the back, I project these scenes into a pair of cheat cards and a face plate, and finally using fluted strips of white alloyed molding I divide the designs into framed panels and hold it all together using bronze rivets to create the beautiful and intimidating Anglo-Saxon battle helm that is my nightly monologue. Sometimes, sometimes, folks. Sometimes, just sometimes, I come to my senses fully naked on the deck of a pirate, besieged, melee, container ship that picked me up floating on the detached door of a port of potty in the Indian Ocean. Then, after a sunstroke induced realization of the crew of this ship plans to sell me and exchange for a bag of oranges to fight off scurvy, I lead a mutiny using only a PVC pipe and a pool chain that accepting my new role as captain and declaring myself King of the Windark Seas. I grab a dirty mop bucket covered in barnacles and adorn it with the teeth of the vanquished to create these shopping wet pirate crown of news that is my segment. Meanwhile, young man.', " Folks, if you watch this show, you know I spend most of my time right over there, carefully blending for you the day's newsiest, most topical flower eggs, milk and butter. And straining into a fine batter to make delicate and informative comedy pancakes, then I glaze them in the juice and zest of the most relevant midnight valencio oranges. And doubts at all, and I find delimane de voyage cognac, before from bang and basting them tables, I deserve you the James Beard Award worthy creeps to ZET. That is my nightly monologue, but sometimes sometimes folks, I wake up in the baggage hole of Greyhound bus. It's being hoisted by the scrapyard claw toward the burn pit. Escape to a nearby abandoned price chopper where I scrounge for old bread scraps, busted open bags of starfruit candies and expired eggs. Chuck it all on a dirty hubcap and slap it over a tire fire before using the legs of a strained pair of sweat pants. As ovenmets to extract and serve the demented transience pound cake of news that is my segment.", " Folks, if you watch the show and I hope you do, I spend a lot of time right over there. Tirelessly studying the lineage of the day's most important thoroughbred stories and whole-stiner headlines, working with the best trainers money can buy to rear their comedy offspring with a hand that is stern yet gentle into the triple crown winning equine specimen that is my nightly monologue. But sometimes sometimes folks I break into an unincorporated veterinary genetics lab. And grab whatever test tubes I can find and then under a grow light I got from a discarded chia pet. I mixed the pill for DNA of a horse and whatever was in a tube labelled Keith Cole and extra. Slurring the concoction with caffeine pills and a microwave bread bowl, I screamed sing a prayer to Janice initiator of human life and God of transformation as a half horse, half man freak, seizes to life before me and the hideous collection of loose animal parts and corrupted men tissue that is my segment. Meanwhile!", ] # fmt: on processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") model = model.to(torch_device) ds = load_dataset("distil-whisper/meanwhile", "default")["test"] ds = ds.cast_column("audio", Audio(sampling_rate=16000)) num_samples = 8 audio = ds[:num_samples]["audio"] audios = [x["array"] for x in audio] inputs = processor( audios, return_tensors="pt", truncation=False, padding="longest", return_attention_mask=True, sampling_rate=16_000, ) inputs = inputs.to(device=torch_device) gen_kwargs = { "return_timestamps": True, "no_speech_threshold": 0.6, "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), "compression_ratio_threshold": 1.35, "condition_on_prev_tokens": True, "logprob_threshold": -2.0, # necessary to avoid triggering temp fallback that will introduce randomness since we are comparing to openai EXTECTED_TEXT "num_beams": 5, "renormalize_logits": True, # necessary to match OAI beam search implementation } set_seed(0) result = model.generate(**inputs, **gen_kwargs) decoded_all = processor.batch_decode(result, skip_special_tokens=True) self.assertListEqual(decoded_all, EXPECTED_TEXT) @slow def test_whisper_shortform_multi_batch_hard_prev_cond(self): # Without this set here, this test may fail if it is run with other tests (say, `test_tiny_*`). It's unclear # why other tests may affect this tests: it seems some random operations are beyond the scene. set_seed(0) # fmt: off EXPECTED_TEXT = [ " Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.", " Nor is Mr. Quilters' manner less interesting than his matter.", " He tells us that at this festive season of the year with Christmas and roast beef looming before us, similarly drawn from eating and its results occur most readily to the mind.", " He has grave doubts whether Sir Frederick Layton's work is really Greek after all and can discover in it but little of Rocky Ithaca.", " Lennils, pictures are a sort of upguards and atom paintings, and Mason's exquisite idles are as national as a jingo poem. Mr. Birkut Foster's landscapes smile at one much in the same way that Mr. Carker used to flash his teeth. And Mr. John Colier gives his visitor a cheerful slap on the back before he says like a shampoo or a turkish bath. Next man!", " It is obviously unnecessary for us to point out how luminous these criticisms are, how delicate and expression.", " On the general principles of art and Mr. Quilter writes with equal lucidity.", " Painting he tells us is of a different quality to mathematics and finish in art is adding more effect.", ] # fmt: on processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") model = model.to(torch_device) ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") num_samples = 8 audio = ds[:num_samples]["audio"] audios = [x["array"] for x in audio] inputs = processor( audios, return_tensors="pt", sampling_rate=16_000, ) inputs = inputs.to(device=torch_device) gen_kwargs = { "return_timestamps": True, "no_speech_threshold": 0.6, "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), "compression_ratio_threshold": 1.35, "condition_on_prev_tokens": True, "logprob_threshold": -1.0, } result = model.generate(**inputs, **gen_kwargs) decoded_all = processor.batch_decode(result, skip_special_tokens=True) self.assertListEqual(decoded_all, EXPECTED_TEXT) @slow def test_whisper_longform_no_speech_detection(self): # fmt: off EXPECTED_TEXT = [ " Folks, if you watch the show, you know, I spent a lot of time right over there. Patiently and astutely scrutinizing the boxwood and mahogany chest set of the day's biggest stories. Developing the central headline pawns, definitely maneuvering and also topical night to F6.", " Folks, I spent a lot of time right over there night after night, actually. Carefully selecting for you the day's newsiest, most aerodynamic headlines, stress testing", ' Ladies and gentlemen, you know, I spent a lot of time right over there raising the finest Holstein news cattle firmly yet tenderly milking the latest headlines from their joke swollen teats', ' Folks, you watched this show, you know I spend most of my time right over there, carefully sorting through the days, big stories, and selecting only the most subtle and unblemished ostrich and crocodile news leather, which I then entrust to artisan graduates of the', " You know, folks, I spent a lot of time crafting for you a bespoke playlist of the day's big stories right over there. meticulously selecting the most topical chakra affirming scented candles, using Feng Shui,", ' You know, folks, I spend most of my time right over there. Mining the days, biggest, most important stories, collecting the finest, most topical iron or hand hammering it into joke panels, then I craft sheets of bronze and blazing with patterns that tell an epic tale of conquest.', " Folks, if you watch this show, you know I spend most of my time right over there, carefully blending for you the day's newsiest, most topical flower eggs, milk and butter. And straining into a fine batter to make delicate and informative comedy pancakes, then I glaze them in the juice and zest of the most...", " Folks, if you watch the show and I hope you do, I spent a lot of time right over there. Tirelessly studying the lineage of the day's most important thoroughbred stories and whole-stiner headlines.", ] # fmt: on processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") model = model.to(torch_device) ds = load_dataset("distil-whisper/meanwhile", "default")["test"] ds = ds.cast_column("audio", Audio(sampling_rate=16000)) num_samples = 8 audio = ds[:num_samples]["audio"] audios = [x["array"] for x in audio] # Make sure the second chunk is silent for audio in audios: audio[15 * 16000 : 60 * 16000] = 0.0 inputs = processor( audios, return_tensors="pt", truncation=False, padding="longest", return_attention_mask=True, sampling_rate=16_000, ) inputs = inputs.to(device=torch_device) gen_kwargs = { "return_timestamps": True, "no_speech_threshold": 0.2, "temperature": (0.0,), "compression_ratio_threshold": 1.35, "condition_on_prev_tokens": True, "logprob_threshold": 0.0, # Ignore logprob, use only no-speech prob "num_beams": 5, } torch.manual_seed(0) result = model.generate(**inputs, **gen_kwargs) decoded_all = processor.batch_decode(result, skip_special_tokens=True) self.assertListEqual(decoded_all, EXPECTED_TEXT) @require_torch_accelerator @slow def test_whisper_empty_longform(self): processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") model = model.to(torch_device) ds = load_dataset("distil-whisper/meanwhile", "default")["test"] ds = ds.cast_column("audio", Audio(sampling_rate=16000)) num_samples = 8 audio = ds[:num_samples]["audio"] audios = [x["array"] for x in audio] audios[0][:] = np.zeros(audios[0].shape) inputs = processor( audios, return_tensors="pt", truncation=False, padding="longest", return_attention_mask=True, sampling_rate=16_000, ) inputs = inputs.to(device=torch_device) gen_kwargs = { "no_speech_threshold": 0.2, "temperature": (0.0,), "logprob_threshold": 0.0, # Ignore logprob, use only no-speech prob "num_beams": 5, "language": "fr", "task": "transcribe", "return_timestamps": True, } torch.manual_seed(0) model.generate(**inputs, **gen_kwargs) @require_torch_multi_accelerator @slow def test_whisper_empty_longform_multi_gpu(self): processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny", device_map="auto") ds = load_dataset("distil-whisper/meanwhile", "default")["test"] ds = ds.cast_column("audio", Audio(sampling_rate=16000)) num_samples = 8 audio = ds[:num_samples]["audio"] audios = [x["array"] for x in audio] audios[0][:] = np.zeros(audios[0].shape) inputs = processor( audios, return_tensors="pt", truncation=False, padding="longest", return_attention_mask=True, sampling_rate=16_000, ) inputs = inputs.to(device=model.device) gen_kwargs = { "no_speech_threshold": 0.2, "temperature": (0.0,), "logprob_threshold": 0.0, # Ignore logprob, use only no-speech prob "num_beams": 5, "language": "fr", "task": "transcribe", "return_timestamps": True, } torch.manual_seed(0) model.generate(**inputs, **gen_kwargs) @slow def test_tiny_static_generation(self): processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") model.to(torch_device) input_speech = self._load_datasamples(4) input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features input_features = input_features.to(torch_device) eager_generated_ids = model.generate(input_features, max_new_tokens=64) # Using statiic cache compiles forward for each decoding step, so we don't have to manually compile model.generation_config.cache_implementation = "static" # compile the forward pass and assert equivalence static_generated_ids = model.generate(input_features, max_new_tokens=64) self.assertTrue((eager_generated_ids == static_generated_ids).all()) # check the compiled graph can be re-used and that the cache is correctly reset # reverse the ordering of the input features permutation_idx = ( torch.arange(input_features.shape[0], 0, step=-1, dtype=torch.long, device=input_features.device) - 1 ) input_features = input_features[permutation_idx, ...] static_generated_ids = model.generate(input_features, max_new_tokens=64) # assert re-ordered generations match those from eager self.assertTrue((eager_generated_ids[permutation_idx, :] == static_generated_ids).all()) @slow def test_tiny_static_generation_long_form(self): import torch._dynamo.config # only permit 4 compilations: 2 prefill steps and 2 decoding steps (1 for each of conditioned/not conditioned) torch._dynamo.config.cache_size_limit = 4 torch._dynamo.reset() processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") model.to(torch_device) dataset = load_dataset("distil-whisper/meanwhile", "default")["test"] dataset = dataset.cast_column("audio", Audio(sampling_rate=16000)) input_speech = [audio["array"] for audio in dataset[2:4]["audio"]] inputs = processor( input_speech, return_tensors="pt", padding="longest", truncation=False, return_attention_mask=True, sampling_rate=16_000, ) inputs = inputs.to(torch_device) gen_kwargs = { "return_timestamps": True, "no_speech_threshold": 0.6, "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), "compression_ratio_threshold": 1.35, "condition_on_prev_tokens": True, # conditioning on prev tokens introduces a recompile on the second time step "logprob_threshold": -1.0, "num_beams": 1, } set_seed(42) eager_generated_ids = model.generate(**inputs, **gen_kwargs) # Using statiic cache compiles forward for each decoding step, so we don't have to manually compile model.generation_config.cache_implementation = "static" set_seed(42) static_generated_ids = model.generate(**inputs, **gen_kwargs) self.assertTrue((eager_generated_ids == static_generated_ids).all()) # check the compiled graph can be re-used and that the cache is correctly reset # reverse the ordering of the input features input_features = inputs.input_features permutation_idx = ( torch.arange(input_features.shape[0], 0, step=-1, dtype=torch.long, device=input_features.device) - 1 ) input_features = input_features[permutation_idx, ...] attention_mask = inputs.attention_mask[permutation_idx, ...] set_seed(42) static_generated_ids = model.generate(input_features, attention_mask=attention_mask, **gen_kwargs) # assert re-ordered generations match those from eager self.assertTrue((eager_generated_ids[permutation_idx, :] == static_generated_ids).all()) @require_torch class WhisperEncoderModelTester: def __init__( self, parent, batch_size=3, # need batch_size != num_hidden layers seq_length=60, is_training=True, use_labels=True, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, input_channels=1, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, max_source_positions=30, num_mel_bins=80, num_conv_layers=1, suppress_tokens=None, classifier_proj_size=4, num_labels=2, is_encoder_decoder=False, is_decoder=False, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.input_channels = input_channels self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.num_mel_bins = num_mel_bins self.max_position_embeddings = max_position_embeddings self.max_source_positions = max_source_positions self.num_conv_layers = num_conv_layers self.suppress_tokens = suppress_tokens self.classifier_proj_size = classifier_proj_size self.num_labels = num_labels self.is_encoder_decoder = is_encoder_decoder self.is_decoder = is_decoder def get_config(self): return WhisperConfig( d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, input_channels=self.input_channels, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, max_source_positions=self.max_source_positions, decoder_ffn_dim=self.hidden_size, encoder_ffn_dim=self.hidden_size, suppress_tokens=self.suppress_tokens, classifier_proj_size=self.classifier_proj_size, num_labels=self.num_labels, is_encoder_decoder=self.is_encoder_decoder, is_decoder=self.is_decoder, ) def prepare_config_and_inputs(self): input_features = floats_tensor([self.batch_size, self.num_mel_bins, self.seq_length]) config = self.get_config() inputs_dict = {"input_features": input_features} return config, inputs_dict def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def get_subsampled_output_lengths(self, input_lengths): """ Computes the output length of the convolutional layers """ for i in range(self.num_conv_layers): input_lengths = (input_lengths - 1) // 2 + 1 return input_lengths @property def encoder_seq_length(self): return self.get_subsampled_output_lengths(self.seq_length) def create_and_check_model_forward(self, config, inputs_dict, use_weighted_layer_sum=False): config.use_weighted_layer_sum = use_weighted_layer_sum model = WhisperForAudioClassification(config=config) model.to(torch_device).eval() input_features = inputs_dict["input_features"] with torch.no_grad(): last_hidden_state = model(input_features).logits self.parent.assertTrue(last_hidden_state.shape, (13, 2)) @require_torch class WhisperEncoderModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (WhisperForAudioClassification,) if is_torch_available() else () is_encoder_decoder = False test_missing_keys = False def setUp(self): self.model_tester = WhisperEncoderModelTester(self) self.config_tester = ConfigTester(self, config_class=WhisperConfig) self.maxDiff = 3000 def test_config(self): self.config_tester.run_common_tests() def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["input_features", "encoder_outputs"] self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) def test_forward_pass(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_forward(*config_and_inputs) def test_forward_pass_weighted_layer_sum(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_forward(*config_and_inputs, use_weighted_layer_sum=True) @unittest.skip(reason="Not applicable for an encoder-only acoustic model") def test_inputs_embeds(self): # input embeds is meaningless for an encoder-only acoustic model pass # the equivalent test is passing the encoder outputs directly to the model def test_encoder_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) with torch.no_grad(): outputs = model(**inputs)[0] encoder = model.encoder encoder_inputs = {"input_features": inputs["input_features"]} del inputs["input_features"] if "attention_mask" in inputs: encoder_inputs["attention_mask"] = inputs["attention_mask"] if "output_attentions" in inputs: encoder_inputs["output_attentions"] = inputs["output_attentions"] with torch.no_grad(): inputs["encoder_outputs"] = encoder(**encoder_inputs) outputs_embeds = model(**inputs)[0] self.assertTrue((outputs_embeds == outputs).all()) # Needs to override as the encoder input embedding is a Conv1d def test_model_get_set_embeddings(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (torch.nn.Conv1d)) model.set_input_embeddings(torch.nn.Conv1d(10, 10, 3)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, torch.nn.Conv1d)) # WhisperEncoder cannot resize token embeddings since it has no tokens embeddings @unittest.skip(reason="Model has no tokens embeds") def test_resize_tokens_embeddings(self): pass class WhisperStandaloneDecoderModelTester: def __init__( self, parent, batch_size=3, # need batch_size != num_hidden layers is_training=True, use_labels=False, vocab_size=200, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, input_channels=1, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, max_source_positions=30, max_target_positions=40, bos_token_id=98, eos_token_id=98, pad_token_id=0, num_mel_bins=80, decoder_start_token_id=85, num_conv_layers=1, suppress_tokens=None, ): self.parent = parent self.batch_size = batch_size self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.input_channels = input_channels self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.num_mel_bins = num_mel_bins self.max_position_embeddings = max_position_embeddings self.max_source_positions = max_source_positions self.max_target_positions = max_target_positions self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.decoder_start_token_id = decoder_start_token_id self.num_conv_layers = num_conv_layers self.suppress_tokens = suppress_tokens def prepare_config_and_inputs(self): input_features = floats_tensor([self.batch_size, self.num_mel_bins, self.seq_length], self.vocab_size) decoder_input_ids = torch.tensor( self.batch_size * [[self.decoder_start_token_id, 3, 3, 7, 2]], device=torch_device ) config = self.get_config() config.is_encoder_decoder = False inputs_dict = prepare_whisper_inputs_dict( config, attention_mask=None, input_features=input_features, decoder_input_ids=decoder_input_ids, ) inputs_dict.pop("input_features") inputs_dict["attention_mask"] = inputs_dict.pop("decoder_attention_mask") inputs_dict["input_ids"] = inputs_dict.pop("decoder_input_ids") return config, inputs_dict @property def encoder_seq_length(self): return 5 @property def seq_length(self): return 5 def get_config(self): return WhisperConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, input_channels=self.input_channels, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, max_source_positions=self.max_source_positions, max_target_positions=self.max_target_positions, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, decoder_ffn_dim=self.hidden_size, encoder_ffn_dim=self.hidden_size, decoder_start_token_id=self.decoder_start_token_id, suppress_tokens=self.suppress_tokens, ) def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() inputs_dict["input_ids"][:, -1] = self.pad_token_id return config, inputs_dict def create_and_check_decoder_model_past(self, config, input_ids): config.use_cache = True model = WhisperDecoder(config=config).to(torch_device).eval() # first forward pass outputs = model(input_ids, use_cache=True) outputs_use_cache_conf = model(input_ids) outputs_no_past = model(input_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) past_key_values = outputs["past_key_values"] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) output_from_no_past = model(next_input_ids)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_decoder_model_attention_mask_past(self, config, input_ids): model = WhisperDecoder(config=config).to(torch_device).eval() # create attention mask attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) half_seq_length = input_ids.shape[-1] // 2 attn_mask[:, half_seq_length:] = 0 # first forward pass past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1 random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1) input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens # append to next input_ids and attn_mask next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) attn_mask = torch.cat( [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1, ) # get two different outputs output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"] output_from_past = model(next_tokens, attention_mask=attn_mask, past_key_values=past_key_values)[ "last_hidden_state" ] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) @require_torch class WhisperStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (WhisperDecoder, WhisperForCausalLM) if is_torch_available() else () is_encoder_decoder = False test_missing_keys = False def setUp(self): self.model_tester = WhisperStandaloneDecoderModelTester(self, is_training=False) self.config_tester = ConfigTester(self, config_class=WhisperConfig) def test_config(self): self.config_tester.run_common_tests() def test_decoder_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() config, inputs_dict = config_and_inputs self.model_tester.create_and_check_decoder_model_past(config=config, input_ids=inputs_dict["input_ids"]) def test_decoder_model_attn_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() config, inputs_dict = config_and_inputs self.model_tester.create_and_check_decoder_model_attention_mask_past( config=config, input_ids=inputs_dict["input_ids"] ) @unittest.skip(reason="Decoder can't keep attention grads") def test_retain_grad_hidden_states_attentions(self): return @unittest.skip(reason="Decoder cannot keep gradients") def test_flex_attention_with_grads(): return