openai開源的語音轉(zhuǎn)文字支持多語言在huggingface中使用例子。
目前發(fā)現(xiàn)多語言模型large-v2支持中文是繁體，因此需要繁體轉(zhuǎn)簡體。
后續(xù)編寫微調(diào)訓練例子

GitHub地址：
https://github.com/openai/whisper

!pip install zhconv
!pip install whisper
!pip install tqdm
!pip install ffmpeg-python
!pip install transformers
!pip install librosa

from transformers import WhisperProcessor, WhisperForConditionalGeneration

import librosa
import torch
from zhconv import convert
import warnings

warnings.filterwarnings("ignore")

audio_file = f"test.wav"
#load audio file
audio, sampling_rate = librosa.load(audio_file, sr=16_000)

# # audio
# display.Audio(audio_file, autoplay=True)

# load model and processor
processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2")
tokenizer = WhisperProcessor.from_pretrained("openai/whisper-large-v2")

processor.save_pretrained("openai/model/whisper-large-v2")
model.save_pretrained("openai/model/whisper-large-v2")
tokenizer.save_pretrained("openai/model/whisper-large-v2")

processor = WhisperProcessor.from_pretrained("openai/model/whisper-large-v2")
model = WhisperForConditionalGeneration.from_pretrained("openai/model/whisper-large-v2")
tokenizer = WhisperProcessor.from_pretrained("openai/model/whisper-large-v2")


# load dummy dataset and read soundfiles
# ds = load_dataset("common_voice", "fr", split="test", streaming=True)
# ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000))
# input_speech = next(iter(ds))["audio"]["array"]
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="zh", task="transcribe")
input_features = processor(audio, return_tensors="pt").input_features
predicted_ids = model.generate(input_features)
# transcription = processor.batch_decode(predicted_ids)

transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
print(transcription)
print('轉(zhuǎn)化為簡體結(jié)果：', convert(transcription, 'zh-cn'))

It is strongly recommended to pass the `sampling_rate` argument to this function. Failing to do so can result in silent errors that might be hard to debug.


['啟動開始錄音']
轉(zhuǎn)化為簡體結(jié)果： 啟動開始錄音

input_features = processor(audio, return_tensors="pt").input_features
predicted_ids = model.generate(input_features)
# transcription = processor.batch_decode(predicted_ids)

transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
print(transcription)
print('轉(zhuǎn)化為簡體結(jié)果：', convert(transcription, 'zh-cn'))

It is strongly recommended to pass the `sampling_rate` argument to this function. Failing to do so can result in silent errors that might be hard to debug.


['啟動開始錄音']
轉(zhuǎn)化為簡體結(jié)果： 啟動開始錄音

#長文本如下
#使用參考網(wǎng)站：https://huggingface.co/openai/whisper-large-v2

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