Vegam Whisper Medium Ml

🚀 vegam-whipser-medium-ml (വേഗം)

This project converts thennal/whisper-medium-ml into the CTranslate2 model format. It enables the use of this model in CTranslate2 or related projects like faster-whisper, facilitating automatic speech recognition tasks.

⚠️ Important Note

The model file size is 3.06 GB.

🚀 Quick Start

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

✨ Features

• Audio Processing: Specialized for audio data, suitable for automatic speech recognition tasks.
• Language Support: Supports Malayalam (ml), expanding its application scope in language processing.
• Model Compatibility: Converted to the CTranslate2 format, ensuring compatibility with related projects.

📦 Installation

1. Install faster-whisper

Install faster-whisper. More details about installation can be found here in faster-whisper.

pip install faster-whisper

2. Install git-lfs

Install git-lfs for using this project. Other approaches for downloading git-lfs in non-debian based systems. Note that git-lfs is just for downloading model from hugging-face.

apt-get install git-lfs

3. Download the model weights

git lfs install
git clone https://huggingface.co/kurianbenoy/vegam-whisper-medium-ml

💻 Usage Examples

Basic 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))

Advanced Usage

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

Note: The audio file 00b38e80-80b8-4f70-babf-566e848879fc.webm is from Malayalam Speech Corpus and is stored along with model weights.

📚 Documentation

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

📄 License

This project is licensed under the MIT license.

👏 Acknowledgments

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

📋 Metadata