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 convert_s3prl_checkpoint(base_model_name, config_path, checkpoint_path,
model_dump_path):
"""
Copy/paste/tweak model's weights to transformers design.
"""
checkpoint = torch.load(checkpoint_path, map_location="cpu")
if checkpoint["Config"]["downstream_expert"]["modelrc"][
"select"] not in SUPPORTED_MODELS:
raise NotImplementedError(
f"The supported s3prl models are {SUPPORTED_MODELS}")
downstream_dict = checkpoint["Downstream"]
hf_congfig = HubertConfig.from_pretrained(config_path)
hf_model = HubertForSequenceClassification.from_pretrained(
base_model_name, config=hf_congfig)
hf_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
base_model_name, return_attention_mask=True, do_normalize=False)
if hf_congfig.use_weighted_layer_sum:
hf_model.layer_weights.data = checkpoint["Featurizer"]["weights"]
hf_model.projector.weight.data = downstream_dict["projector.weight"]
hf_model.projector.bias.data = downstream_dict["projector.bias"]
hf_model.classifier.weight.data = downstream_dict[
"model.post_net.linear.weight"]
hf_model.classifier.bias.data = downstream_dict[
"model.post_net.linear.bias"]
hf_feature_extractor.save_pretrained(model_dump_path)
hf_model.save_pretrained(model_dump_path)
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)