openai开源的whisper在huggingface中使用例子（语音转文字中文）_huggingface whisper

openai开源的语音转文字支持多语言在huggingface中使用例子。
目前发现多语言模型large-v2支持中文是繁体，因此需要繁体转简体。
后续编写微调训练例子

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('转化为简体结果：', convert(transcription, 'zh-cn'))
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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.


['启动开始录音']
转化为简体结果： 启动开始录音
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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('转化为简体结果：', convert(transcription, 'zh-cn'))
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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.


['启动开始录音']
转化为简体结果： 启动开始录音
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#长文本如下
#使用参考网站：https://huggingface.co/openai/whisper-large-v2
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