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vegam-whisper-medium-ml

Automatic Speech Recognition
Malayalam
ctranslate2
audio
vegam
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metadata
language:
  - ml
tags:
  - audio
  - automatic-speech-recognition
license: mit
datasets:
  - google/fleurs
  - thennal/IMaSC
  - mozilla-foundation/common_voice_11_0
library_name: ctranslate2

vegam-whipser-medium-ml

This is a conversion of thennal/whisper-medium-ml to the CTranslate2 model format.

This model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper.

Installation 

pip install faster-whisper
  • Install git-lfs for using this project. Note that git-lfs is just for downloading model from hugging-face.
apt-get install git-lfs
  • Download the model weights
git lfs install
git clone https://huggingface.co/kurianbenoy/vegam-whisper-medium-ml

Usage 

from 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))

Example 

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

Conversion Details

This conversion was possible with wonderful CTranslate2 library leveraging the Transformers converter for OpenAI Whisper.The original model was converted with the following command:

ct2-transformers-converter --model thennal/whisper-medium-ml --output_dir vegam-whisper-medium-ml

Many Thanks to

  • Creators of CTranslate2 and faster-whisper
  • Thennal D K
  • Santhosh Thottingal