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README.md
(简体中文|English)
Introduction
Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.
Whisper model trained by OpenAI whisper https://github.com/openai/whisper
Usage
1. Installation
see installation.
You can choose one way from easy, meduim and hard to install paddlespeech.
2. Prepare Input File
The input of this demo should be a WAV file(.wav
), and the sample rate must be the same as the model.
Here are sample files for this demo that can be downloaded:
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
3. Usage
-
Command Line(Recommended)
# to recognize text paddlespeech whisper --task transcribe --input ./zh.wav # to change model English-Only base size model paddlespeech whisper --lang en --size base --task transcribe --input ./en.wav # to recognize text and translate to English paddlespeech whisper --task translate --input ./zh.wav
Usage:
paddlespeech whisper --help
Arguments:
input
(required): Audio file to recognize.model
: Model type of asr task. Default:whisper-large
.task
: Output type. Default:transcribe
.lang
: Model language. Default: ``. Useen
to choice English-only model. Now [medium,base,small,tiny] size can support English-only.size
: Model size for decode. Defalut:large
. Now can support [large,medium,base,small,tiny].language
: Set decode language. Default:None
. Forcibly set the recognized language, which is determined by the model itself by default.sample_rate
: Sample rate of the model. Default:16000
. Other sampling rates are not supported now.config
: Config of asr task. Use pretrained model when it is None. Default:None
.ckpt_path
: Model checkpoint. Use pretrained model when it is None. Default:None
.yes
: No additional parameters required. Once set this parameter, it means accepting the request of the program by default, which includes transforming the audio sample rate. Default:False
.device
: Choose device to execute model inference. Default: default device of paddlepaddle in current environment.verbose
: Show the log information.
-
Python API
import paddle from paddlespeech.cli.whisper import WhisperExecutor whisper_executor = WhisperExecutor() # to recognize text text = whisper_executor( model='whisper', task='transcribe', sample_rate=16000, config=None, # Set `config` and `ckpt_path` to None to use pretrained model. ckpt_path=None, audio_file='./zh.wav', device=paddle.get_device()) print('ASR Result: \n{}'.format(text)) # to recognize text and translate to English feature = whisper_executor( model='whisper', task='translate', sample_rate=16000, config=None, # Set `config` and `ckpt_path` to None to use pretrained model. ckpt_path=None, audio_file='./zh.wav', device=paddle.get_device()) print('Representation: \n{}'.format(feature))
Output:
Transcribe Result: Detected language: Chinese [00:00.000 --> 00:05.000] 我认为跑步最重要的就是给我带来了身体健康 {'text': '我认为跑步最重要的就是给我带来了身体健康', 'segments': [{'id': 0, 'seek': 0, 'start': 0.0, 'end': 5.0, 'text': '我认为跑步最重要的就是给我带来了身体健康', 'tokens': [50364, 1654, 7422, 97, 13992, 32585, 31429, 8661, 24928, 1546, 5620, 49076, 4845, 99, 34912, 19847, 29485, 44201, 6346, 115, 50614], 'temperature': 0.0, 'avg_logprob': -0.23577967557040128, 'compression_ratio': 0.28169014084507044, 'no_speech_prob': 0.028302080929279327}], 'language': 'zh'} Translate Result: Detected language: Chinese [00:00.000 --> 00:05.000] I think the most important thing about running is that it brings me good health. {'text': ' I think the most important thing about running is that it brings me good health.', 'segments': [{'id': 0, 'seek': 0, 'start': 0.0, 'end': 5.0, 'text': ' I think the most important thing about running is that it brings me good health.', 'tokens': [50364, 286, 519, 264, 881, 1021, 551, 466, 2614, 307, 300, 309, 5607, 385, 665, 1585, 13, 50614], 'temperature': 0.0, 'avg_logprob': -0.47945233395225123, 'compression_ratio': 1.095890410958904, 'no_speech_prob': 0.028302080929279327}], 'language': 'zh'}