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large-v3-turbo model (#2361)

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Jong Wook Kim
2024-09-30 10:59:51 -07:00
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README.md
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@@ -57,17 +57,21 @@ pip install setuptools-rust
## Available models and languages
There are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Below are the names of the available models and their approximate memory requirements and inference speed relative to the large model; actual speed may vary depending on many factors including the available hardware.
There are six model sizes, four with English-only versions, offering speed and accuracy tradeoffs.
Below are the names of the available models and their approximate memory requirements and inference speed relative to the large model.
The relative speeds below are measured by transcribing English speech on a A100, and the real-world speed may vary significantly depending on many factors including the language, the speaking speed, and the available hardware.
| Size | Parameters | English-only model | Multilingual model | Required VRAM | Relative speed |
|:------:|:----------:|:------------------:|:------------------:|:-------------:|:--------------:|
| tiny | 39 M | `tiny.en` | `tiny` | ~1 GB | ~32x |
| base | 74 M | `base.en` | `base` | ~1 GB | ~16x |
| small | 244 M | `small.en` | `small` | ~2 GB | ~6x |
| tiny | 39 M | `tiny.en` | `tiny` | ~1 GB | ~10x |
| base | 74 M | `base.en` | `base` | ~1 GB | ~7x |
| small | 244 M | `small.en` | `small` | ~2 GB | ~4x |
| medium | 769 M | `medium.en` | `medium` | ~5 GB | ~2x |
| large | 1550 M | N/A | `large` | ~10 GB | 1x |
| turbo | 809 M | N/A | `turbo` | ~6 GB | ~8x |
The `.en` models for English-only applications tend to perform better, especially for the `tiny.en` and `base.en` models. We observed that the difference becomes less significant for the `small.en` and `medium.en` models.
Additionally, the `turbo` model is an optimized version of `large-v3` that offers faster transcription speed with a minimal degradation in accuracy.
Whisper's performance varies widely depending on the language. The figure below shows a performance breakdown of `large-v3` and `large-v2` models by language, using WERs (word error rates) or CER (character error rates, shown in *Italic*) evaluated on the Common Voice 15 and Fleurs datasets. Additional WER/CER metrics corresponding to the other models and datasets can be found in Appendix D.1, D.2, and D.4 of [the paper](https://arxiv.org/abs/2212.04356), as well as the BLEU (Bilingual Evaluation Understudy) scores for translation in Appendix D.3.
@@ -77,9 +81,9 @@ Whisper's performance varies widely depending on the language. The figure below
## Command-line usage
The following command will transcribe speech in audio files, using the `medium` model:
The following command will transcribe speech in audio files, using the `turbo` model:
whisper audio.flac audio.mp3 audio.wav --model medium
whisper audio.flac audio.mp3 audio.wav --model turbo
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:
@@ -103,7 +107,7 @@ Transcription can also be performed within Python:
```python
import whisper
model = whisper.load_model("base")
model = whisper.load_model("turbo")
result = model.transcribe("audio.mp3")
print(result["text"])
```
@@ -115,7 +119,7 @@ Below is an example usage of `whisper.detect_language()` and `whisper.decode()`
```python
import whisper
model = whisper.load_model("base")
model = whisper.load_model("turbo")
# load audio and pad/trim it to fit 30 seconds
audio = whisper.load_audio("audio.mp3")