faster-whisper officially supports the large-v3 model now. The link is Systran/faster-whisper-large-v3
README.md file is based on "guillaumekln/faster-whisper-large-v2" and has been updated to version 3 content.
Whisper large-v3 model for CTranslate2
This repository contains the conversion of openai/whisper-large-v3 to the CTranslate2 model format.
This model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper.
Example
from faster_whisper import WhisperModel
model = WhisperModel("large-v3")
segments, info = model.transcribe("audio.mp3")
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
Conversion details
The original model was converted with the following command:
ct2-transformers-converter --model openai/whisper-large-v3 --output_dir faster-whisper-large-v3 \
--copy_files added_tokens.json special_tokens_map.json tokenizer_config.json vocab.json --quantization float16
Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the compute_type
option in CTranslate2.
Note that while "openai/whisper-large-v3" does not come with a "tokenizer.json" file, you can generate it using AutoTokenizer.
from transformers import AutoTokenizer
self.hf_tokenizer = AutoTokenizer.from_pretrained("openai/whisper-large-v3")
self.hf_tokenizer.save_pretrained("whisper-large-v3-test")
How faster-whisper working with Whisper-large-v3
In faster-whisper version 0.10.0, there is no need to perform this handling.
Working with Whisper-large-v3 #547 by. UmarRamzan
- from faster_whisper import WhisperModel
- model = WhisperModel(model_url)
- if "large-v3" in model_url:
- model.feature_extractor.mel_filters = model.feature_extractor.get_mel_filters(model.feature_extractor.sampling_rate, model.feature_extractor.n_fft, n_mels=128)
More information
For more information about the original model, see its model card.
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