Instructions to use zh-plus/faster-whisper-large-v2-japanese-5k-steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zh-plus/faster-whisper-large-v2-japanese-5k-steps with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="zh-plus/faster-whisper-large-v2-japanese-5k-steps")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zh-plus/faster-whisper-large-v2-japanese-5k-steps", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Converted from clu-ling/whisper-large-v2-japanese-5k-steps using CTranslate2.
Usage:
Install
pip install faster-whisper(Check faster-whisper for detailed instructions.)from faster_whisper import WhisperModel model = WhisperModel('zh-plus/faster-whisper-large-v2-japanese-5k-steps', device="cuda", compute_type="float16") 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))
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