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Guillaume Klein
2023-04-25 15:47:38 +02:00
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@@ -64,8 +64,6 @@ GPU execution requires the NVIDIA libraries cuBLAS 11.x and cuDNN 8.x to be inst
## Usage ## Usage
### Library
```python ```python
from faster_whisper import WhisperModel from faster_whisper import WhisperModel
@@ -94,7 +92,7 @@ segments, _ = model.transcribe("audio.mp3")
segments = list(segments) # The transcription will actually run here. segments = list(segments) # The transcription will actually run here.
``` ```
#### Word-level timestamps ### Word-level timestamps
```python ```python
segments, _ = model.transcribe("audio.mp3", word_timestamps=True) segments, _ = model.transcribe("audio.mp3", word_timestamps=True)
@@ -104,7 +102,7 @@ for segment in segments:
print("[%.2fs -> %.2fs] %s" % (word.start, word.end, word.word)) print("[%.2fs -> %.2fs] %s" % (word.start, word.end, word.word))
``` ```
#### VAD filter ### VAD filter
The library integrates the [Silero VAD](https://github.com/snakers4/silero-vad) model to filter out parts of the audio without speech: The library integrates the [Silero VAD](https://github.com/snakers4/silero-vad) model to filter out parts of the audio without speech:
@@ -118,13 +116,26 @@ The default behavior is conservative and only removes silence longer than 2 seco
segments, _ = model.transcribe("audio.mp3", vad_filter=True, vad_parameters=dict(min_silence_duration_ms=500)) segments, _ = model.transcribe("audio.mp3", vad_filter=True, vad_parameters=dict(min_silence_duration_ms=500))
``` ```
#### Going further ### Logging
The library logging level can be configured like this:
```python
import logging
logging.basicConfig()
logging.getLogger("faster_whisper").setLevel(logging.DEBUG)
```
### Going further
See more model and transcription options in the [`WhisperModel`](https://github.com/guillaumekln/faster-whisper/blob/master/faster_whisper/transcribe.py) class implementation. See more model and transcription options in the [`WhisperModel`](https://github.com/guillaumekln/faster-whisper/blob/master/faster_whisper/transcribe.py) class implementation.
### CLI ## Community integrations
You can use [jordimas/whisper-ctranslate2](https://github.com/jordimas/whisper-ctranslate2) to access `faster-whisper` through a CLI interface similar to what is offered by Whisper. Here is a non exhaustive list of open-source projects using *faster-whisper*. Feel free to add your project to the list!
* [whisper-ctranslate2](https://github.com/jordimas/whisper-ctranslate2) is a command line client based on `faster-whisper` and compatible with the original client from openai/whisper.
## Model conversion ## Model conversion