[s2t] add whisper asr large model (#2640)
* add whisper asr large model decoding, test=asr * fix code style. * fix json code style. * remove resource and fix code style. * fix yapf * add cli and demos, fix some code style. * fix some problem by comment. * fix yapfpull/2670/head
zxcd
2 years ago
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([简体中文](./README_cn.md)|English)
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## Introduction
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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.
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Whisper model trained by OpenAI whisper https://github.com/openai/whisper
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## Usage
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### 1. Installation
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see [installation](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md).
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You can choose one way from easy, meduim and hard to install paddlespeech.
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### 2. Prepare Input File
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The input of this demo should be a WAV file(`.wav`), and the sample rate must be the same as the model.
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Here are sample files for this demo that can be downloaded:
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```bash
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wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
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```
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### 3. Usage
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- Command Line(Recommended)
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```bash
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# to recognize text
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paddlespeech whisper --task transcribe --input ./zh.wav
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# to recognize text and translate to English
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paddlespeech whisper --task translate --input ./zh.wav
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```
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Usage:
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```bash
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paddlespeech whisper --help
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```
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Arguments:
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- `input`(required): Audio file to recognize.
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- `model`: Model type of asr task. Default: `whisper-large`.
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- `task`: Output type. Default: `transcribe`.
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- `lang`: Model language. Default: `None`. Forcibly set the recognized language, which is determined by the model itself by default.
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- `sample_rate`: Sample rate of the model. Default: `16000`. Other sampling rates are not supported now.
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- `config`: Config of asr task. Use pretrained model when it is None. Default: `None`.
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- `ckpt_path`: Model checkpoint. Use pretrained model when it is None. Default: `None`.
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- `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`.
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- `device`: Choose device to execute model inference. Default: default device of paddlepaddle in current environment.
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- `verbose`: Show the log information.
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- Python API
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```python
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import paddle
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from paddlespeech.cli.whisper import WhisperExecutor
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whisper_executor = WhisperExecutor()
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# to recognize text
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text = whisper_executor(
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model='whisper-large',
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task='transcribe',
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sample_rate=16000,
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config=None, # Set `config` and `ckpt_path` to None to use pretrained model.
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ckpt_path=None,
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audio_file='./zh.wav',
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device=paddle.get_device())
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print('ASR Result: \n{}'.format(text))
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# to recognize text and translate to English
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feature = whisper_executor(
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model='whisper-large',
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task='translate',
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sample_rate=16000,
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config=None, # Set `config` and `ckpt_path` to None to use pretrained model.
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ckpt_path=None,
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audio_file='./zh.wav',
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device=paddle.get_device())
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print('Representation: \n{}'.format(feature))
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```
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Output:
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```bash
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Transcribe Result:
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Detected language: Chinese
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[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
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{'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'}
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Translate Result:
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Detected language: Chinese
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[00:00.000 --> 00:05.000] I think the most important thing about running is that it brings me good health.
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{'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'}
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#!/bin/bash
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# audio download
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wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
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# to recognize text
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paddlespeech whisper --task transcribe --input ./zh.wav
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# to recognize text and translate to English
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paddlespeech whisper --task translate --input ./zh.wav
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from .infer import WhisperExecutor
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.∏
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Modified from Whisper (https://github.com/openai/whisper/whisper/)
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import os.path
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import sys
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import distutils
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import numpy as np
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import paddle
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import soundfile
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from yacs.config import CfgNode
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from paddlespeech.s2t.models.whisper import log_mel_spectrogram
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from paddlespeech.s2t.models.whisper import ModelDimensions
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from paddlespeech.s2t.models.whisper import transcribe
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from paddlespeech.s2t.models.whisper import Whisper
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from paddlespeech.s2t.training.cli import default_argument_parser
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from paddlespeech.s2t.utils.log import Log
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logger = Log(__name__).getlog()
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class WhisperInfer():
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def __init__(self, config, args):
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self.args = args
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self.config = config
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self.audio_file = args.audio_file
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paddle.set_device('gpu' if self.args.ngpu > 0 else 'cpu')
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config.pop("ngpu")
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#load_model
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model_dict = paddle.load(self.config.model_file)
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config.pop("model_file")
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dims = ModelDimensions(**model_dict["dims"])
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self.model = Whisper(dims)
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self.model.load_dict(model_dict)
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def run(self):
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check(args.audio_file)
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with paddle.no_grad():
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temperature = config.pop("temperature")
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temperature_increment_on_fallback = config.pop(
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"temperature_increment_on_fallback")
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if temperature_increment_on_fallback is not None:
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temperature = tuple(
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np.arange(temperature, 1.0 + 1e-6,
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temperature_increment_on_fallback))
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else:
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temperature = [temperature]
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#load audio
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mel = log_mel_spectrogram(args.audio)
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result = transcribe(
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self.model, mel, temperature=temperature, **config)
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if args.result_file is not None:
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with open(args.result_file, 'w') as f:
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f.write(str(result))
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return result
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def check(audio_file: str):
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if not os.path.isfile(audio_file):
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print("Please input the right audio file path")
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sys.exit(-1)
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logger.info("checking the audio file format......")
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try:
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_, sample_rate = soundfile.read(audio_file)
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except Exception as e:
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logger.error(str(e))
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logger.error(
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"can not open the wav file, please check the audio file format")
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sys.exit(-1)
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logger.info("The sample rate is %d" % sample_rate)
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assert (sample_rate == 16000)
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logger.info("The audio file format is right")
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def main(config, args):
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WhisperInfer(config, args).run()
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if __name__ == "__main__":
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parser = default_argument_parser()
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# save asr result to
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parser.add_argument(
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"--result_file", type=str, help="path of save the asr result")
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parser.add_argument(
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"--audio_file", type=str, help="path of the input audio file")
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parser.add_argument(
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"--debug",
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type=distutils.util.strtobool,
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default=False,
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help="for debug.")
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args = parser.parse_args()
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config = CfgNode(new_allowed=True)
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if args.config:
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config.merge_from_file(args.config)
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if args.decode_cfg:
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decode_confs = CfgNode(new_allowed=True)
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decode_confs.merge_from_file(args.decode_cfg)
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config.decode = decode_confs
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if args.opts:
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config.merge_from_list(args.opts)
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config.freeze()
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main(config, args)
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@ -0,0 +1,12 @@
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# MIT License, Copyright (c) 2022 OpenAI.
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# Copyright (c) 2022 PaddlePaddle Authors and . All Rights Reserved.
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#
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# Modified from OpenAI Whisper 2022 (https://github.com/openai/whisper/whisper/__init__.py)
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from paddlespeech.s2t.models.whisper.whipser import decode
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from paddlespeech.s2t.models.whisper.whipser import DecodingOptions
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from paddlespeech.s2t.models.whisper.whipser import DecodingResult
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from paddlespeech.s2t.models.whisper.whipser import detect_language
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from paddlespeech.s2t.models.whisper.whipser import log_mel_spectrogram
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from paddlespeech.s2t.models.whisper.whipser import ModelDimensions
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from paddlespeech.s2t.models.whisper.whipser import transcribe
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from paddlespeech.s2t.models.whisper.whipser import Whisper
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# MIT License, Copyright (c) 2022 OpenAI.
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# Copyright (c) 2022 PaddlePaddle Authors and . All Rights Reserved.
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#
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# Modified from OpenAI Whisper 2022 (https://github.com/openai/whisper/whisper/tokenizer.py)
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import os
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from dataclasses import dataclass
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from functools import lru_cache
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from typing import List
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from typing import Optional
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from typing import Tuple
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from typing import Union
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import numpy as np
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import paddle
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from paddlenlp.transformers import GPTTokenizer
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LANGUAGES = {
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"en": "english",
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"zh": "chinese",
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"de": "german",
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"es": "spanish",
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"ru": "russian",
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"ko": "korean",
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"fr": "french",
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"ja": "japanese",
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"pt": "portuguese",
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"tr": "turkish",
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"pl": "polish",
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"ca": "catalan",
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"nl": "dutch",
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"ar": "arabic",
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"sv": "swedish",
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"it": "italian",
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"id": "indonesian",
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"hi": "hindi",
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"fi": "finnish",
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"vi": "vietnamese",
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"iw": "hebrew",
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"uk": "ukrainian",
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"el": "greek",
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"ms": "malay",
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"cs": "czech",
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"ro": "romanian",
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"da": "danish",
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"hu": "hungarian",
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"ta": "tamil",
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"no": "norwegian",
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"th": "thai",
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"ur": "urdu",
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"hr": "croatian",
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"bg": "bulgarian",
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"lt": "lithuanian",
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"la": "latin",
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"mi": "maori",
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"ml": "malayalam",
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"cy": "welsh",
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"sk": "slovak",
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"te": "telugu",
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"fa": "persian",
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"lv": "latvian",
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"bn": "bengali",
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"sr": "serbian",
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"az": "azerbaijani",
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"sl": "slovenian",
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"kn": "kannada",
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"et": "estonian",
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"mk": "macedonian",
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"br": "breton",
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"eu": "basque",
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"is": "icelandic",
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"hy": "armenian",
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"ne": "nepali",
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"mn": "mongolian",
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"bs": "bosnian",
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"kk": "kazakh",
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"sq": "albanian",
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"sw": "swahili",
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"gl": "galician",
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"mr": "marathi",
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"pa": "punjabi",
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"si": "sinhala",
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"km": "khmer",
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"sn": "shona",
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"yo": "yoruba",
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"so": "somali",
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"af": "afrikaans",
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"oc": "occitan",
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"ka": "georgian",
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"be": "belarusian",
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"tg": "tajik",
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"sd": "sindhi",
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"gu": "gujarati",
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"am": "amharic",
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"yi": "yiddish",
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"lo": "lao",
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"uz": "uzbek",
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"fo": "faroese",
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"ht": "haitian creole",
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"ps": "pashto",
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"tk": "turkmen",
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"nn": "nynorsk",
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"mt": "maltese",
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"sa": "sanskrit",
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"lb": "luxembourgish",
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"my": "myanmar",
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"bo": "tibetan",
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"tl": "tagalog",
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"mg": "malagasy",
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"as": "assamese",
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"tt": "tatar",
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"haw": "hawaiian",
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"ln": "lingala",
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"ha": "hausa",
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"ba": "bashkir",
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"jw": "javanese",
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"su": "sundanese",
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}
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# language code lookup by name, with a few language aliases
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TO_LANGUAGE_CODE = {
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**{language: code for code, language in LANGUAGES.items()},
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"burmese": "my",
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"valencian": "ca",
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"flemish": "nl",
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"haitian": "ht",
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"letzeburgesch": "lb",
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"pushto": "ps",
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"panjabi": "pa",
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"moldavian": "ro",
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"moldovan": "ro",
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"sinhalese": "si",
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"castilian": "es",
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}
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@dataclass(frozen=True)
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class Tokenizer:
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"""A thin wrapper around `GPTTokenizer` providing quick access to special tokens"""
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tokenizer: "GPTTokenizer"
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language: Optional[str]
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sot_sequence: Tuple[int]
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def encode(self, text, **kwargs):
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return self.tokenizer.encode(text, **kwargs)
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def decode(self,
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token_ids: Union[int, List[int], np.ndarray, paddle.Tensor],
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**kwargs):
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if len(token_ids) > 1:
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ids_list = []
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for ids in token_ids:
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if paddle.is_tensor(ids):
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ids = ids.item()
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if ids < len(self.tokenizer):
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ids_list.append(ids)
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token_ids = ids_list
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return self.tokenizer.decode(token_ids, **kwargs)
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def decode_with_timestamps(self, tokens) -> str:
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"""
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Timestamp tokens are above the special tokens' id range and are ignored by `decode()`.
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This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>".
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"""
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outputs = [[]]
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for token in tokens:
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if token >= self.timestamp_begin:
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timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>"
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outputs.append(timestamp)
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outputs.append([])
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else:
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outputs[-1].append(token)
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outputs = [
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s if isinstance(s, str) else self.tokenizer.decode(s)
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for s in outputs
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]
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return "".join(outputs)
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@property
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@lru_cache()
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def eot(self) -> int:
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return self.tokenizer.eos_token_id
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@property
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@lru_cache()
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def sot(self) -> int:
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return self._get_single_token_id("<|startoftranscript|>")
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|
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@property
|
||||
@lru_cache()
|
||||
def sot_lm(self) -> int:
|
||||
return self._get_single_token_id("<|startoflm|>")
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def sot_prev(self) -> int:
|
||||
return self._get_single_token_id("<|startofprev|>")
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def no_speech(self) -> int:
|
||||
return self._get_single_token_id("<|nospeech|>")
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def no_timestamps(self) -> int:
|
||||
return self._get_single_token_id("<|notimestamps|>")
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def timestamp_begin(self) -> int:
|
||||
return self.tokenizer.all_special_ids[-1] + 1
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def language_token(self) -> int:
|
||||
"""Returns the token id corresponding to the value of the `language` field"""
|
||||
if self.language is None:
|
||||
raise ValueError(
|
||||
"This tokenizer does not have language token configured")
|
||||
|
||||
additional_tokens = dict(
|
||||
zip(
|
||||
self.tokenizer.additional_special_tokens,
|
||||
self.tokenizer.additional_special_tokens_ids, ))
|
||||
candidate = f"<|{self.language}|>"
|
||||
if candidate in additional_tokens:
|
||||
return additional_tokens[candidate]
|
||||
|
||||
raise KeyError(f"Language {self.language} not found in tokenizer.")
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def all_language_tokens(self) -> Tuple[int]:
|
||||
result = []
|
||||
for token, token_id in zip(
|
||||
self.tokenizer.additional_special_tokens,
|
||||
self.tokenizer.additional_special_tokens_ids, ):
|
||||
if token.strip("<|>") in LANGUAGES:
|
||||
result.append(token_id)
|
||||
return tuple(result)
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def all_language_codes(self) -> Tuple[str]:
|
||||
return tuple(
|
||||
self.decode([l]).strip("<|>") for l in self.all_language_tokens)
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def sot_sequence_including_notimestamps(self) -> Tuple[int]:
|
||||
return tuple(list(self.sot_sequence) + [self.no_timestamps])
|
||||
|
||||
@property
|
||||
@lru_cache()
|
||||
def non_speech_tokens(self) -> Tuple[int]:
|
||||
"""
|
||||
Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech
|
||||
annotations, to prevent sampling texts that are not actually spoken in the audio, e.g.
|
||||
|
||||
- ♪♪♪
|
||||
- ( SPEAKING FOREIGN LANGUAGE )
|
||||
- [DAVID] Hey there,
|
||||
|
||||
keeping basic punctuations like commas, periods, question marks, exclamation points, etc.
|
||||
"""
|
||||
symbols = list("\"#()*+/:;<=>@[\\]^_`{|}~「」『』")
|
||||
symbols += "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split(
|
||||
)
|
||||
|
||||
# symbols that may be a single token or multiple tokens depending on the tokenizer.
|
||||
# In case they're multiple tokens, suppress the first token, which is safe because:
|
||||
# These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress
|
||||
# in generations, and in the 3-byte UTF-8 representation they share the first two bytes.
|
||||
miscellaneous = set("♩♪♫♬♭♮♯")
|
||||
assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous)
|
||||
|
||||
# allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word
|
||||
result = {
|
||||
self.tokenizer.encode(" -").input_ids[0],
|
||||
self.tokenizer.encode(" '").input_ids[0]
|
||||
}
|
||||
for symbol in symbols + list(miscellaneous):
|
||||
for tokens in [
|
||||
self.tokenizer.encode(symbol).input_ids,
|
||||
self.tokenizer.encode(" " + symbol).input_ids
|
||||
]:
|
||||
if len(tokens) == 1 or symbol in miscellaneous:
|
||||
result.add(tokens[0])
|
||||
|
||||
return tuple(sorted(result))
|
||||
|
||||
def _get_single_token_id(self, text) -> int:
|
||||
tokens = self.tokenizer.encode(text).input_ids
|
||||
assert len(tokens) == 1, f"{text} is not encoded as a single token"
|
||||
return tokens[0]
|
||||
|
||||
|
||||
@lru_cache(maxsize=None)
|
||||
def build_tokenizer(name: str="gpt2"):
|
||||
os.environ["TOKENIZERS_PARALLELISM"] = "false"
|
||||
path = os.path.join(os.path.dirname(__file__), "assets", name)
|
||||
tokenizer = GPTTokenizer.from_pretrained(path)
|
||||
|
||||
specials = [
|
||||
"<|startoftranscript|>",
|
||||
* [f"<|{lang}|>" for lang in LANGUAGES.keys()],
|
||||
"<|translate|>",
|
||||
"<|transcribe|>",
|
||||
"<|startoflm|>",
|
||||
"<|startofprev|>",
|
||||
"<|nospeech|>",
|
||||
"<|notimestamps|>",
|
||||
]
|
||||
|
||||
tokenizer.add_special_tokens(dict(additional_special_tokens=specials))
|
||||
return tokenizer
|
||||
|
||||
|
||||
@lru_cache(maxsize=None)
|
||||
def get_tokenizer(
|
||||
multilingual: bool,
|
||||
*,
|
||||
task: Optional[str]=None, # Literal["transcribe", "translate", None]
|
||||
language: Optional[str]=None, ) -> Tokenizer:
|
||||
if language is not None:
|
||||
language = language.lower()
|
||||
if language not in LANGUAGES:
|
||||
if language in TO_LANGUAGE_CODE:
|
||||
language = TO_LANGUAGE_CODE[language]
|
||||
else:
|
||||
raise ValueError(f"Unsupported language: {language}")
|
||||
|
||||
if multilingual:
|
||||
tokenizer_name = "multilingual"
|
||||
task = task or "transcribe"
|
||||
language = language or "en"
|
||||
else:
|
||||
tokenizer_name = "gpt2"
|
||||
task = None
|
||||
language = None
|
||||
|
||||
tokenizer = build_tokenizer(name=tokenizer_name)
|
||||
all_special_ids: List[int] = tokenizer.all_special_ids
|
||||
sot: int = all_special_ids[1]
|
||||
translate: int = all_special_ids[-6]
|
||||
transcribe: int = all_special_ids[-5]
|
||||
|
||||
langs = tuple(LANGUAGES.keys())
|
||||
sot_sequence = [sot]
|
||||
if language is not None:
|
||||
sot_sequence.append(sot + 1 + langs.index(language))
|
||||
if task is not None:
|
||||
sot_sequence.append(transcribe if task == "transcribe" else translate)
|
||||
|
||||
return Tokenizer(
|
||||
tokenizer=tokenizer,
|
||||
language=language,
|
||||
sot_sequence=tuple(sot_sequence))
|
||||
@ -0,0 +1,92 @@
|
||||
# MIT License, Copyright (c) 2022 OpenAI.
|
||||
# Copyright (c) 2022 PaddlePaddle Authors and . All Rights Reserved.
|
||||
#
|
||||
# Modified from OpenAI Whisper 2022 (https://github.com/openai/whisper/whisper/utils.py)
|
||||
import zlib
|
||||
from typing import Iterator
|
||||
from typing import TextIO
|
||||
|
||||
|
||||
def exact_div(x, y):
|
||||
assert x % y == 0
|
||||
return x // y
|
||||
|
||||
|
||||
def str2bool(string):
|
||||
str2val = {"True": True, "False": False}
|
||||
if string in str2val:
|
||||
return str2val[string]
|
||||
else:
|
||||
raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}")
|
||||
|
||||
|
||||
def optional_int(string):
|
||||
return None if string == "None" else int(string)
|
||||
|
||||
|
||||
def optional_float(string):
|
||||
return None if string == "None" else float(string)
|
||||
|
||||
|
||||
def compression_ratio(text) -> float:
|
||||
return len(text) / len(zlib.compress(text.encode("utf-8")))
|
||||
|
||||
|
||||
def format_timestamp(seconds: float,
|
||||
always_include_hours: bool=False,
|
||||
decimal_marker: str='.'):
|
||||
assert seconds >= 0, "non-negative timestamp expected"
|
||||
milliseconds = round(seconds * 1000.0)
|
||||
|
||||
hours = milliseconds // 3_600_000
|
||||
milliseconds -= hours * 3_600_000
|
||||
|
||||
minutes = milliseconds // 60_000
|
||||
milliseconds -= minutes * 60_000
|
||||
|
||||
seconds = milliseconds // 1_000
|
||||
milliseconds -= seconds * 1_000
|
||||
|
||||
hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
|
||||
return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
|
||||
|
||||
|
||||
def write_txt(transcript: Iterator[dict], file: TextIO):
|
||||
for segment in transcript:
|
||||
print(segment['text'].strip(), file=file, flush=True)
|
||||
|
||||
|
||||
def write_vtt(transcript: Iterator[dict], file: TextIO):
|
||||
print("WEBVTT\n", file=file)
|
||||
for segment in transcript:
|
||||
print(
|
||||
f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n"
|
||||
f"{segment['text'].strip().replace('-->', '->')}\n",
|
||||
file=file,
|
||||
flush=True, )
|
||||
|
||||
|
||||
def write_srt(transcript: Iterator[dict], file: TextIO):
|
||||
"""
|
||||
Write a transcript to a file in SRT format.
|
||||
|
||||
Example usage:
|
||||
from pathlib import Path
|
||||
from whisper.utils import write_srt
|
||||
|
||||
result = transcribe(model, audio_path, temperature=temperature, **args)
|
||||
|
||||
# save SRT
|
||||
audio_basename = Path(audio_path).stem
|
||||
with open(Path(output_dir) / (audio_basename + ".srt"), "w", encoding="utf-8") as srt:
|
||||
write_srt(result["segments"], file=srt)
|
||||
"""
|
||||
for i, segment in enumerate(transcript, start=1):
|
||||
# write srt lines
|
||||
print(
|
||||
f"{i}\n"
|
||||
f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> "
|
||||
f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n"
|
||||
f"{segment['text'].strip().replace('-->', '->')}\n",
|
||||
file=file,
|
||||
flush=True, )
|
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,21 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2022 OpenAI
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
Loading…
Reference in new issue