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Note: Model file size is 3.06 GB
pip install faster-whisperapt-get install git-lfsgit lfs install
git clone https://huggingface.co/kurianbenoy/vegam-whisper-medium-mlfrom faster_whisper import WhisperModel
model_path = "vegam-whisper-medium-ml"
# Run on GPU with FP16
model = WhisperModel(model_path, device="cuda", compute_type="float16")
# or run on GPU with INT8
# model = WhisperModel(model_path, device="cuda", compute_type="int8_float16")
# or run on CPU with INT8
# model = WhisperModel(model_path, device="cpu", compute_type="int8")
segments, info = model.transcribe("audio.mp3", beam_size=5)
print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))from faster_whisper import WhisperModel
model_path = "vegam-whisper-medium-ml"
model = WhisperModel(model_path, device="cuda", compute_type="float16")
segments, info = model.transcribe("00b38e80-80b8-4f70-babf-566e848879fc.webm", beam_size=5)
print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))Detected language 'ta' with probability 0.353516
[0.00s -> 4.74s] പാലം കടുക്കുവോളം നാരായണ പാലം കടന്നാലൊ കൂരായണ
ct2-transformers-converter --model thennal/whisper-medium-ml --output_dir vegam-whisper-medium-ml