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)
