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license: mit | |
Pretrained on 10k hours WenetSpeech L subset. More details in [TencentGameMate/chinese_speech_pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain) | |
This model does not have a tokenizer as it was pretrained on audio alone. | |
In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. | |
python package: | |
transformers==4.16.2 | |
```python | |
import torch | |
import torch.nn.functional as F | |
import soundfile as sf | |
from transformers import ( | |
Wav2Vec2FeatureExtractor, | |
HubertModel, | |
) | |
model_path="" | |
wav_path="" | |
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_path) | |
model = HubertModel.from_pretrained(model_path) | |
# for pretrain: Wav2Vec2ForPreTraining | |
# model = Wav2Vec2ForPreTraining.from_pretrained(model_path) | |
model = model.to(device) | |
model = model.half() | |
model.eval() | |
wav, sr = sf.read(wav_path) | |
input_values = feature_extractor(wav, return_tensors="pt").input_values | |
input_values = input_values.half() | |
input_values = input_values.to(device) | |
with torch.no_grad(): | |
outputs = model(input_values) | |
last_hidden_state = outputs.last_hidden_state | |
``` |