# coding=utf-8 # 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 TensorFlow Whisper model. """ from __future__ import annotations import inspect import tempfile import traceback import unittest import numpy as np from transformers import WhisperConfig, WhisperFeatureExtractor, WhisperProcessor from transformers.testing_utils import is_tf_available, require_tf, require_tokenizers, run_test_in_subprocess, slow from transformers.utils import cached_property from transformers.utils.import_utils import is_datasets_available from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_datasets_available(): import datasets from datasets import load_dataset if is_tf_available(): import tensorflow as tf from transformers import TFWhisperForConditionalGeneration, TFWhisperModel, set_seed from transformers.models.whisper.modeling_tf_whisper import ( TFWhisperDecoder, TFWhisperEncoder, sinusoidal_embedding_init, ) def prepare_whisper_inputs_dict( config, input_features, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if decoder_attention_mask is None: decoder_attention_mask = tf.where(decoder_input_ids != config.pad_token_id, 1, 0) if head_mask is None: head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads)) if decoder_head_mask is None: decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) if cross_attn_head_mask is None: cross_attn_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) return { "input_features": input_features, "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } @require_tf class TFWhisperModelTester: def __init__( self, parent, batch_size=13, 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=60, 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, begin_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 self.begin_suppress_tokens = begin_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 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) 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, begin_suppress_tokens=self.begin_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): model = TFWhisperModel(config=config) 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 = TFWhisperModel(config=config).get_decoder() # take a slice so we're shorter than the seqeuence length and can append later input_ids = inputs_dict["decoder_input_ids"][:, :-10] attention_mask = inputs_dict["decoder_attention_mask"][:, :-10] # 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_token = ids_tensor((self.batch_size, 3), config.vocab_size) next_tokens = tf.where(next_token <= 2, 2, next_token) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-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 = np.random.randint(0, output_from_past.shape[-1]) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(np.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2)) def check_encoder_decoder_model_standalone(self, config, inputs_dict): model = TFWhisperModel(config=config) 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 = TFWhisperEncoder.from_pretrained(tmpdirname) 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() < 1e-3) with tempfile.TemporaryDirectory() as tmpdirname: decoder = model.get_decoder() decoder.save_pretrained(tmpdirname) decoder = TFWhisperDecoder.from_pretrained(tmpdirname) 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() < 1e-3) @require_tf class TFWhisperModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (TFWhisperModel, TFWhisperForConditionalGeneration) if is_tf_available() else () all_generative_model_classes = (TFWhisperForConditionalGeneration,) if is_tf_available() else () pipeline_model_mapping = {"feature-extraction": TFWhisperModel} if is_tf_available() else {} is_encoder_decoder = True fx_compatible = False test_pruning = False test_missing_keys = False test_onnx = False input_name = "input_features" # TODO (ydshieh): undo skip once a fix is done on TF side. @unittest.skip("Skip for now as TF 2.13 breaks it on GPU") def test_xla_generate_slow(self): super().test_xla_generate_slow() def setUp(self): self.model_tester = TFWhisperModelTester(self) self.config_tester = ConfigTester(self, config_class=WhisperConfig) self.maxDiff = 3000 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) model.build() with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname, saved_model=False) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], []) 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_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.trainable) 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) model.build() embeds = model.get_encoder().embed_positions.get_weights()[0] sinusoids = sinusoidal_embedding_init(embeds.shape).numpy() self.assertTrue(np.allclose(embeds, sinusoids)) 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 _get_input_ids_and_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_ids = inputs_dict[self.input_name] # cut to half length & take max batch_size 3 max_batch_size = 3 input_ids = input_ids[:max_batch_size, :, :] # generate max 3 tokens max_length = 4 if config.eos_token_id is not None and config.pad_token_id is None: # hack to allow generate for models such as GPT2 as is done in `generate()` config.pad_token_id = config.eos_token_id return config, input_ids, None, max_length # not implemented currently def test_inputs_embeds(self): pass @unittest.skip("Training is not yet supported") def test_training(self): pass def test_generate_with_head_masking(self): pass @unittest.skip("fp16 is not yet supported for TF models") 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 = TFWhisperForConditionalGeneration(config) model.generate(input_features) model.generate(input_features, num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3) 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.call) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = [ "input_features", "decoder_input_ids", "decoder_attention_mask", ] expected_arg_names.extend( ["decoder_position_ids", "head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"] if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names else ["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) 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", seq_length) 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 check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=5e-5, name="outputs", attributes=None): # We override with a slightly higher tol value, as test recently became flaky super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) decoder_key_length = getattr(self.model_tester, "decoder_key_length", encoder_key_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(config) 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) 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) 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) 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_generate_without_input_ids(self): pass @staticmethod def _get_encoder_outputs( model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1 ): encoder = model.get_encoder() encoder_outputs = encoder( input_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.repeat_interleave( num_interleave, dim=0 ) input_ids = input_ids[:, :, 0] input_ids = tf.zeros_like(input_ids[:, :1], dtype=tf.int64) + tf.convert_to_tensor( [model._get_decoder_start_token_id()] ) attention_mask = None return encoder_outputs, input_ids, attention_mask def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1): batch_size, mel, seq_length = input_ids.shape subsampled_seq_length = self.model_tester.get_subsampled_output_lengths(seq_length) num_sequences_in_output = batch_size * num_return_sequences gen_len = ( output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length ) # scores self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config) # Attentions # encoder self._check_encoder_attention_for_generate( output.encoder_attentions, batch_size, config, subsampled_seq_length ) # decoder self._check_attentions_for_generate( num_sequences_in_output, output.decoder_attentions, min_length=1, max_length=output.sequences.shape[-1], config=config, use_cache=use_cache, ) # Hidden States # encoder self._check_encoder_hidden_states_for_generate( output.encoder_hidden_states, batch_size, config, subsampled_seq_length ) # decoder self._check_hidden_states_for_generate( num_sequences_in_output, output.decoder_hidden_states, min_length=1, max_length=output.sequences.shape[-1], config=config, use_cache=use_cache, ) # overwritten from parent due to the inability to work when non-text inputs are not passed AND because the input is # `input_features` def test_lm_head_model_random_no_beam_search_generate(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_features = inputs_dict.get("input_features", None) # iterate over all generative models for model_class in self.all_generative_model_classes: model = model_class(config) if config.bos_token_id is None: # if bos token id is not defined model needs input_features with self.assertRaises(AssertionError): model.generate(do_sample=True, max_length=5) # num_return_sequences = 1 self._check_generated_ids(model.generate(input_features, do_sample=True)) with self.assertRaises(ValueError): # generating multiple sequences when no beam search generation # is not allowed as it would always generate the same sequences model.generate(input_features, do_sample=False, num_return_sequences=2) # num_return_sequences > 1, sample self._check_generated_ids(model.generate(input_features, do_sample=True, num_return_sequences=2)) # check bad words tokens language generation # create list of 1-seq bad token and list of 2-seq of bad tokens bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)] output_tokens = model.generate( input_features, do_sample=True, bad_words_ids=bad_words_ids, num_return_sequences=2 ) # only count generated tokens generated_ids = output_tokens[:, input_features.shape[-1] :] self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids)) # overwritten from parent due to the inability to work when non-text inputs are not passed AND because the input is # `input_features` def test_lm_head_model_random_beam_search_generate(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_features = inputs_dict.get("input_features", None) for model_class in self.all_generative_model_classes: model = model_class(config) if config.bos_token_id is None: # if bos token id is not defined model needs input_ids, num_return_sequences = 1 self._check_generated_ids(model.generate(input_features, do_sample=True, num_beams=2)) with self.assertRaises(ValueError): # generating more sequences than having beams leads is not possible model.generate(input_features, do_sample=False, num_return_sequences=3, num_beams=2) # num_return_sequences > 1, sample self._check_generated_ids( model.generate( input_features, do_sample=True, num_beams=2, num_return_sequences=2, ) ) # num_return_sequences > 1, greedy self._check_generated_ids( model.generate(input_features, do_sample=False, num_beams=2, num_return_sequences=2) ) # check bad words tokens language generation # create list of 1-seq bad token and list of 2-seq of bad tokens bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)] output_tokens = model.generate( input_features, do_sample=False, bad_words_ids=bad_words_ids, num_beams=2, num_return_sequences=2 ) # only count generated tokens generated_ids = output_tokens[:, input_features.shape[-1] :] self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids)) def test_generate_with_prompt_ids_and_task_and_language(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() model = TFWhisperForConditionalGeneration(config) input_features = input_dict["input_features"] prompt_ids = np.arange(5) language = "<|de|>" task = "translate" lang_id = 6 task_id = 7 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, max_new_tokens=5, task=task, language=language, prompt_ids=prompt_ids) expected_output_start = [ *prompt_ids.tolist(), model.generation_config.decoder_start_token_id, lang_id, task_id, ] for row in output.numpy().tolist(): self.assertListEqual(row[: len(expected_output_start)], expected_output_start) def test_generate_with_prompt_ids_and_forced_decoder_ids(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() model = TFWhisperForConditionalGeneration(config) input_features = input_dict["input_features"] prompt_ids = np.asarray(range(5)) forced_decoder_ids = [(1, 6), (2, 7), (3, 8)] output = model.generate( input_features, max_new_tokens=5, forced_decoder_ids=forced_decoder_ids, prompt_ids=prompt_ids ) expected_output_start = [ *prompt_ids.tolist(), model.generation_config.decoder_start_token_id, *[token for _rank, token in forced_decoder_ids], ] for row in output.numpy().tolist(): self.assertListEqual(row[: len(expected_output_start)], expected_output_start) def _load_datasamples(num_samples): ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"] return [x["array"] for x in speech_samples] def _test_large_logits_librispeech(in_queue, out_queue, timeout): error = None try: _ = in_queue.get(timeout=timeout) set_seed(0) model = TFWhisperModel.from_pretrained("openai/whisper-large") input_speech = _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="tf" ) input_features = processed_inputs.input_features decoder_input_ids = processed_inputs.labels 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 @ tf.transpose(model.model.decoder.embed_tokens.weights[0]) # fmt: off EXPECTED_LOGITS = tf.convert_to_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 unittest.TestCase().assertTrue(np.allclose(logits[0, 0, :30], EXPECTED_LOGITS, atol=1e-4)) except Exception: error = f"{traceback.format_exc()}" results = {"error": error} out_queue.put(results, timeout=timeout) out_queue.join() def _test_large_generation(in_queue, out_queue, timeout): error = None try: _ = in_queue.get(timeout=timeout) set_seed(0) processor = WhisperProcessor.from_pretrained("openai/whisper-large") model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-large") input_speech = _load_datasamples(1) input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="tf").input_features 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" unittest.TestCase().assertEqual(transcript, EXPECTED_TRANSCRIPT) except Exception: error = f"{traceback.format_exc()}" results = {"error": error} out_queue.put(results, timeout=timeout) out_queue.join() def _test_large_generation_multilingual(in_queue, out_queue, timeout): error = None try: _ = in_queue.get(timeout=timeout) set_seed(0) processor = WhisperProcessor.from_pretrained("openai/whisper-large") model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-large") ds = load_dataset("common_voice", "ja", 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.feature_extractor(raw_speech=input_speech, return_tensors="tf").input_features generated_ids = model.generate( input_features, do_sample=False, max_length=20, language="<|ja|>", task="transcribe" ) transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] EXPECTED_TRANSCRIPT = "木村さんに電話を貸してもらいました" unittest.TestCase().assertEqual(transcript, EXPECTED_TRANSCRIPT) 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 = " Kimura-san called me." unittest.TestCase().assertEqual(transcript, EXPECTED_TRANSCRIPT) generated_ids = model.generate( input_features, do_sample=False, max_length=20, language="<|ja|>", task="translate" ) transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] EXPECTED_TRANSCRIPT = " I borrowed a phone from Kimura san" unittest.TestCase().assertEqual(transcript, EXPECTED_TRANSCRIPT) except Exception: error = f"{traceback.format_exc()}" results = {"error": error} out_queue.put(results, timeout=timeout) out_queue.join() def _test_large_batched_generation(in_queue, out_queue, timeout): error = None try: _ = in_queue.get(timeout=timeout) set_seed(0) processor = WhisperProcessor.from_pretrained("openai/whisper-large") model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-large") input_speech = _load_datasamples(4) input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="tf").input_features generated_ids_1 = model.generate(input_features[0:2], max_length=20) generated_ids_2 = model.generate(input_features[2:4], max_length=20) generated_ids = np.concatenate([generated_ids_1, generated_ids_2]) # fmt: off EXPECTED_IDS = [ [50258, 50358, 50363, 2221, 13, 2326, 388, 391, 307, 264, 50244, 295, 264, 2808, 5359, 293, 321, 366, 5404, 281], [50258, 50358, 50363, 6966, 307, 2221, 13, 2326, 388, 391, 311, 9060, 1570, 1880, 813, 702, 1871, 13, 50257, 50257], [50258, 50358, 50363, 634, 5112, 505, 300, 412, 341, 42729, 3196, 295, 264, 1064, 11, 365, 5272, 293, 12904, 9256], [50258, 50358, 50363, 634, 575, 12525, 22618, 1968, 6144, 35617, 20084, 1756, 311, 589, 307, 534, 10281, 934, 439, 11] ] # fmt: on unittest.TestCase().assertEqual(generated_ids.tolist(), EXPECTED_IDS) # fmt: off EXPECTED_TRANSCRIPT = [ " Mr. Quilter is the apostle of the middle classes and we are glad to", " 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", " He has grave doubts whether Sir Frederick Layton's work is really Greek after all," ] # fmt: on transcript = processor.batch_decode(generated_ids, skip_special_tokens=True) unittest.TestCase().assertListEqual(transcript, EXPECTED_TRANSCRIPT) except Exception: error = f"{traceback.format_exc()}" results = {"error": error} out_queue.put(results, timeout=timeout) out_queue.join() @require_tf @require_tokenizers class TFWhisperModelIntegrationTests(unittest.TestCase): @cached_property def default_processor(self): return WhisperProcessor.from_pretrained("openai/whisper-base") def _load_datasamples(self, num_samples): return _load_datasamples(num_samples) @slow def test_tiny_logits_librispeech(self): set_seed(0) model = TFWhisperModel.from_pretrained("openai/whisper-tiny") input_speech = self._load_datasamples(1) feature_extractor = WhisperFeatureExtractor() input_features = feature_extractor(input_speech, return_tensors="tf").input_features logits = model( input_features, decoder_input_ids=tf.convert_to_tensor([[50258, 50259, 50359]]), output_hidden_states=False, output_attentions=False, return_dict=False, use_cache=False, ) # fmt: off EXPECTED_LOGITS = tf.convert_to_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 self.assertTrue(np.allclose(logits[0][0, 0, :30], EXPECTED_LOGITS, atol=1e-4)) # fmt: off EXPECTED_GENERATION = tf.convert_to_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] @ tf.transpose(model.model.decoder.embed_tokens.weights[0]) self.assertTrue(np.allclose(head_logits[0, 0, :30], EXPECTED_GENERATION, atol=1e-4)) @slow def test_small_en_logits_librispeech(self): set_seed(0) model = TFWhisperModel.from_pretrained("openai/whisper-small.en") input_speech = self._load_datasamples(1) feaure_extractor = WhisperFeatureExtractor() input_features = feaure_extractor(input_speech, return_tensors="tf").input_features logits = model( input_features, decoder_input_ids=tf.convert_to_tensor([[model.config.decoder_start_token_id]]), output_hidden_states=False, output_attentions=False, use_cache=False, ) logits = logits.last_hidden_state @ tf.transpose(model.model.decoder.embed_tokens.weights[0]) # fmt: off EXPECTED_LOGITS = tf.convert_to_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 self.assertTrue(np.allclose(logits[0, 0, :30], EXPECTED_LOGITS, atol=1e-4)) @slow def test_large_logits_librispeech(self): run_test_in_subprocess(test_case=self, target_func=_test_large_logits_librispeech, inputs=None) @slow def test_tiny_en_generation(self): set_seed(0) processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") model.config.decoder_start_token_id = 50257 input_speech = self._load_datasamples(1) input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="tf").input_features generated_ids = model.generate(input_features, num_beams=5, max_length=20) transcript = processor.tokenizer.batch_decode(generated_ids)[0] EXPECTED_TRANSCRIPT = ( "<|startoftranscript|><|notimestamps|> Mr. Quilter is the apostle of the middle" " classes, and we are glad to" ) self.assertEqual(transcript, EXPECTED_TRANSCRIPT) @slow def test_tiny_generation(self): set_seed(0) processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") input_speech = self._load_datasamples(1) input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="tf").input_features generated_ids = model.generate(input_features, num_beams=5, max_length=20) transcript = processor.tokenizer.decode(generated_ids[0]) EXPECTED_TRANSCRIPT = ( "<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle" " classes and we are glad" ) self.assertEqual(transcript, EXPECTED_TRANSCRIPT) @slow def test_tiny_xla_generation(self): set_seed(0) processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") input_speech = self._load_datasamples(1) input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="tf").input_features xla_generate = tf.function(model.generate, jit_compile=True) generated_ids = model.generate(input_features, num_beams=5, max_length=20) generated_ids_xla = xla_generate(input_features, num_beams=5, max_length=20) transcript = processor.tokenizer.decode(generated_ids[0]) transcript_xla = processor.tokenizer.decode(generated_ids_xla[0]) EXPECTED_TRANSCRIPT = ( "<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle" " classes and we are glad" ) self.assertEqual(transcript, EXPECTED_TRANSCRIPT) self.assertEqual(transcript_xla, EXPECTED_TRANSCRIPT) @slow def test_large_generation(self): run_test_in_subprocess(test_case=self, target_func=_test_large_generation, inputs=None) @slow def test_large_generation_multilingual(self): run_test_in_subprocess(test_case=self, target_func=_test_large_generation_multilingual, inputs=None) @slow def test_large_batched_generation(self): run_test_in_subprocess(test_case=self, target_func=_test_large_batched_generation, inputs=None) @slow def test_tiny_en_batched_generation(self): set_seed(0) processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") input_speech = self._load_datasamples(4) input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="tf").input_features generated_ids = model.generate(input_features, max_length=20) # fmt: off EXPECTED_LOGITS = tf.convert_to_tensor( [ [50257, 50362, 1770, 13, 2264, 346, 353, 318, 262, 46329, 286, 262, 3504, 6097, 11, 290, 356, 389, 9675, 284], [50257, 50362, 5414, 318, 1770, 13, 2264, 346, 353, 338, 5642, 1342, 3499, 621, 465, 2300, 13, 50256, 50256, 50256], [50257, 50362, 679, 4952, 514, 326, 379, 428, 43856, 1622, 286, 262, 614, 11, 351, 6786, 290, 32595, 12023, 28236], [50257, 50362, 679, 468, 12296, 17188, 1771, 7361, 26113, 18881, 1122, 338, 670, 318, 1107, 8312, 706, 477, 290, 460] ] ) # fmt: on self.assertTrue(np.allclose(generated_ids, EXPECTED_LOGITS)) # fmt: off EXPECTED_TRANSCRIPT = [ " Mr. Quilter is the apostle of the middle classes, and we are glad to", " 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", " He has grave doubts whether Sir Frederick Layton'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) @slow def test_tiny_en_batched_xla_generation(self): set_seed(0) processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") input_speech = self._load_datasamples(4) input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="tf").input_features xla_generate = tf.function(model.generate, jit_compile=True) generated_ids = model.generate(input_features, max_length=20) generated_ids_xla = xla_generate(input_features, max_length=20) # fmt: off EXPECTED_LOGITS = tf.convert_to_tensor( [ [50257, 50362, 1770, 13, 2264, 346, 353, 318, 262, 46329, 286, 262, 3504, 6097, 11, 290, 356, 389, 9675, 284], [50257, 50362, 5414, 318, 1770, 13, 2264, 346, 353, 338, 5642, 1342, 3499, 621, 465, 2300, 13, 50256, 50256, 50256], [50257, 50362, 679, 4952, 514, 326, 379, 428, 43856, 1622, 286, 262, 614, 11, 351, 6786, 290, 32595, 12023, 28236], [50257, 50362, 679, 468, 12296, 17188, 1771, 7361, 26113, 18881, 1122, 338, 670, 318, 1107, 8312, 706, 477, 290, 460] ] ) # fmt: on self.assertTrue(np.allclose(generated_ids, EXPECTED_LOGITS)) self.assertTrue(np.allclose(generated_ids_xla, EXPECTED_LOGITS)) # fmt: off EXPECTED_TRANSCRIPT = [ " Mr. Quilter is the apostle of the middle classes, and we are glad to", " 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", " He has grave doubts whether Sir Frederick Layton's work is really Greek after all and can", ] # fmt: on transcript = processor.batch_decode(generated_ids, skip_special_tokens=True) transcript_xla = processor.batch_decode(generated_ids_xla, skip_special_tokens=True) self.assertListEqual(transcript, EXPECTED_TRANSCRIPT) self.assertListEqual(transcript_xla, EXPECTED_TRANSCRIPT)