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docs: updated README to specify translation model limitation (#2547)

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Updated README given info from https://github.com/openai/whisper/discussions/2483
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Nicholas Nadeau, Ph.D., P.Eng. 2025-06-25 20:03:47 -04:00 committed by GitHub
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@ -77,25 +77,35 @@ Whisper's performance varies widely depending on the language. The figure below
![WER breakdown by language](https://github.com/openai/whisper/assets/266841/f4619d66-1058-4005-8f67-a9d811b77c62)
## Command-line usage
The following command will transcribe speech in audio files, using the `turbo` model:
```bash
whisper audio.flac audio.mp3 audio.wav --model turbo
```
The default setting (which selects the `turbo` model) 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 `turbo` model) works well for transcribing English. However, **the `turbo` model is not trained for translation tasks**. If you need to **translate non-English speech into English**, use one of the **multilingual models** (`tiny`, `base`, `small`, `medium`, `large`) instead of `turbo`.
For example, to transcribe an audio file containing non-English speech, you can specify the language:
```bash
whisper japanese.wav --language Japanese
```
Adding `--task translate` will translate the speech into English:
To **translate** speech into English, use:
whisper japanese.wav --language Japanese --task translate
```bash
whisper japanese.wav --model medium --language Japanese --task translate
```
> **Note:** The `turbo` model will return the original language even if `--task translate` is specified. Use `medium` or `large` for the best translation results.
Run the following to view all available options:
```bash
whisper --help
```
See [tokenizer.py](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py) for the list of all available languages.