Comments (4)
Instead of converting from faster-whisper format, you can directly convert the pytorch models to onnx format.
For example, if you want to convert large-v2
, you can use the workflow mentioned in this page on https://huggingface.co/openai/whisper-large-v2
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thank you,I try later
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Hey @lmxin123, did you try? How did it go?
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Hey @lmxin123, did you try? How did it go?
Not yet, I haven't found a way yet
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Related Issues (20)
- With `faster-distil-whisper-large-v3` or `large-v3`, `transcribe` instruction is ignored (it translates instead) HOT 3
- ON arm64 'for segment in segments' run a lot of time HOT 2
- Faster whisper loads the wrong tokenizer for whisper-large-v3 derivatives HOT 2
- Having issue in decoding audio chunks properly for fasterWhisper transcribe func
- finetuning encounter multiple errors on the 2nd step (Fine-tuning XTTS Encoder) HOT 1
- clip_timestamps does not work across multiple files [faster-whisper 1.0.2] HOT 3
- What are the ways to improve the speed of continuously recognizing multiple audio files? HOT 1
- Silero-VAD Meta Hallucinations HOT 1
- Limited GPU Utilization with NVIDIA RTX 4000 Ada Gen HOT 13
- The Japanese conversion to the back has always been show thanks for listening ご視聴ありがとうございました what is the reason
- Batch process available? HOT 2
- Word-level timestamps are off after hotwords is setted HOT 1
- Finetuning with Dora HOT 1
- Is there a method or parameter that can filter out noise that is not human voice? HOT 3
- The VAD parameters and default values in the source code is inconsistent with the description in README.md HOT 1
- how can I get more accurate timestamps? HOT 1
- Can not upload model to hub HOT 1
- TypeError: `pad_width` must be of integral type. HOT 3
- Minimum CUDA version HOT 1
- 有没有大佬遇到过 a problem RuntimeError: Cannot load the vocabulary from the model directory HOT 1
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