def create_and_check_batch_inference(self, config, input_values, *args): # test does not pass for models making use of `group_norm` # check: https://github.com/pytorch/fairseq/issues/3227 model = HubertModel(config=config) model.to(torch_device) model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i]:] = 0.0 attention_mask[i, input_lengths[i]:] = 0.0 batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state for i in range(input_values.shape[0]): input_slice = input_values[i:i + 1, :input_lengths[i]] output = model(input_slice).last_hidden_state batch_output = batch_outputs[i:i + 1, :output.shape[1]] self.parent.assertTrue( torch.allclose(output, batch_output, atol=1e-3))
def convert_hubert_checkpoint(pytorch_dump_folder_path, config_path=None): """ Copy/paste/tweak model's weights to transformers design. """ model = distilhubert().model.model if config_path is not None: config = HubertConfig.from_pretrained(config_path) else: config = convert_config(model) model = model.eval() feature_extractor = Wav2Vec2FeatureExtractor( feature_size=1, sampling_rate=16000, padding_value=0, do_normalize=False, return_attention_mask=False, ) hf_model = HubertModel(config) recursively_load_weights(model, hf_model) feature_extractor.save_pretrained(pytorch_dump_folder_path) hf_model.save_pretrained(pytorch_dump_folder_path)
def create_and_check_model(self, config, input_values, attention_mask): model = HubertModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size))
def convert_hubert_checkpoint(checkpoint_path, pytorch_dump_folder_path, config_path=None, dict_path=None, is_finetuned=True): """ Copy/paste/tweak model's weights to transformers design. """ if config_path is not None: config = HubertConfig.from_pretrained(config_path) else: config = HubertConfig() if is_finetuned: if dict_path: target_dict = Dictionary.load(dict_path) # important change bos & pad token id since CTC symbol is <pad> and # not <s> as in fairseq config.bos_token_id = target_dict.pad_index config.pad_token_id = target_dict.bos_index config.eos_token_id = target_dict.eos_index config.vocab_size = len(target_dict.symbols) vocab_path = os.path.join(pytorch_dump_folder_path, "vocab.json") if not os.path.isdir(pytorch_dump_folder_path): logger.error( "--pytorch_dump_folder_path ({}) should be a directory". format(pytorch_dump_folder_path)) return os.makedirs(pytorch_dump_folder_path, exist_ok=True) with open(vocab_path, "w", encoding="utf-8") as vocab_handle: json.dump(target_dict.indices, vocab_handle) tokenizer = Wav2Vec2CTCTokenizer( vocab_path, unk_token=target_dict.unk_word, pad_token=target_dict.pad_word, bos_token=target_dict.bos_word, eos_token=target_dict.eos_word, word_delimiter_token="|", do_lower_case=False, ) return_attention_mask = True if config.feat_extract_norm == "layer" else False feature_extractor = Wav2Vec2FeatureExtractor( feature_size=1, sampling_rate=16000, padding_value=0, do_normalize=True, return_attention_mask=return_attention_mask, ) processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) processor.save_pretrained(pytorch_dump_folder_path) hf_wav2vec = HubertForCTC(config) else: hf_wav2vec = HubertModel(config) if is_finetuned: model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path], arg_overrides={"data": "/".join(dict_path.split("/")[:-1])}) else: model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path]) model = model[0].eval() recursively_load_weights(model, hf_wav2vec, is_finetuned) hf_wav2vec.save_pretrained(pytorch_dump_folder_path)