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--- |
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language: |
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- ml |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- vegam |
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license: mit |
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datasets: |
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- google/fleurs |
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- thennal/IMaSC |
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- mozilla-foundation/common_voice_11_0 |
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library_name: ctranslate2 |
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--- |
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> Note: Model file size is 3.06 GB |
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# vegam-whipser-medium-ml (വേഗം) |
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This is a conversion of [thennal/whisper-medium-ml](https://huggingface.co/thennal/whisper-medium-ml) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. |
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This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/guillaumekln/faster-whisper). |
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## Installation |
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- Install [faster-whisper](https://github.com/guillaumekln/faster-whisper). More details about installation can be [found here in faster-whisper](https://github.com/guillaumekln/faster-whisper/tree/master#installation). |
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``` |
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pip install faster-whisper |
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``` |
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- Install [git-lfs](https://git-lfs.com/) for using this project. [Other approaches for downloading git-lfs in non-debian based systems](https://github.com/git-lfs/git-lfs?utm_source=gitlfs_site&utm_medium=installation_link&utm_campaign=gitlfs#installing). |
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Note that git-lfs is just for downloading model from hugging-face. |
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``` |
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apt-get install git-lfs |
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``` |
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- Download the model weights |
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``` |
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git lfs install |
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git clone https://huggingface.co/kurianbenoy/vegam-whisper-medium-ml |
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``` |
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## Usage |
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``` |
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from faster_whisper import WhisperModel |
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model_path = "vegam-whisper-medium-ml" |
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# Run on GPU with FP16 |
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model = WhisperModel(model_path, device="cuda", compute_type="float16") |
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# or run on GPU with INT8 |
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# model = WhisperModel(model_path, device="cuda", compute_type="int8_float16") |
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# or run on CPU with INT8 |
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# model = WhisperModel(model_path, device="cpu", compute_type="int8") |
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segments, info = model.transcribe("audio.mp3", beam_size=5) |
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print("Detected language '%s' with probability %f" % (info.language, info.language_probability)) |
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for segment in segments: |
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print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) |
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``` |
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## Example |
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``` |
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from faster_whisper import WhisperModel |
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model_path = "vegam-whisper-medium-ml" |
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model = WhisperModel(model_path, device="cuda", compute_type="float16") |
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segments, info = model.transcribe("00b38e80-80b8-4f70-babf-566e848879fc.webm", beam_size=5) |
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print("Detected language '%s' with probability %f" % (info.language, info.language_probability)) |
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for segment in segments: |
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print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) |
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``` |
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> Detected language 'ta' with probability 0.353516 |
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> [0.00s -> 4.74s] പാലം കടുക്കുവോളം നാരായണ പാലം കടന്നാലൊ കൂരായണ |
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Note: The audio file [00b38e80-80b8-4f70-babf-566e848879fc.webm](https://huggingface.co/kurianbenoy/vegam-whisper-medium-ml/blob/main/00b38e80-80b8-4f70-babf-566e848879fc.webm) is from [Malayalam Speech Corpus](https://blog.smc.org.in/malayalam-speech-corpus/) and is stored along with model weights. |
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## Conversion Details |
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This conversion was possible with wonderful [CTranslate2 library](https://github.com/OpenNMT/CTranslate2) leveraging the [Transformers converter for OpenAI Whisper](https://opennmt.net/CTranslate2/guides/transformers.html#whisper).The original model was converted with the following command: |
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``` |
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ct2-transformers-converter --model thennal/whisper-medium-ml --output_dir vegam-whisper-medium-ml |
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``` |
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## Many Thanks to |
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- Creators of CTranslate2 and faster-whisper |
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- Thennal D K |
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- Santhosh Thottingal |
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