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making small model the default

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Jong Wook Kim 2022-09-22 02:45:03 +09:00
parent 6e3be77e1a
commit 834f00a0ea
2 changed files with 7 additions and 7 deletions
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10
README.md
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@ -37,17 +37,17 @@ choco install ffmpeg
## Command-line usage
The following command will transcribe speech in audio files
The following command will transcribe speech in audio files, using the `medium` model:
whisper audio.flac audio.mp3 audio.wav
whisper audio.flac audio.mp3 audio.wav --model medium
The default setting works well for transcribing English. To transcribe an audio file containing non-English speech, you can specify the language using the `--language` option:
The default setting (which selects the `small` model) works well for transcribing English. To transcribe an audio file containing non-English speech, you can specify the language using the `--language` option:
whisper ~/japanese.wav --language Japanese
whisper japanese.wav --language Japanese
Adding `--task translate` will translate the speech into English:
whisper ~/japanese.wav --language Japanese --task translate
whisper japanese.wav --language Japanese --task translate
Run the following to view all available transcription options:

4
whisper/transcribe.py
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@ -229,13 +229,13 @@ def cli():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe")
parser.add_argument("--model", default="base", choices=available_models(), help="name of the Whisper model to use")
parser.add_argument("--model", default="small", choices=available_models(), help="name of the Whisper model to use")
parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference")
parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
parser.add_argument("--verbose", type=str2bool, default=True, help="Whether to print out the progress and debug messages")
parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')")
parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted(TO_LANGUAGE_CODE.keys()), help="language spoken in the audio, specify None to perform language detection")
parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), help="language spoken in the audio, specify None to perform language detection")
parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling")
parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature")