Audio-Text-to-Text
Transformers
Safetensors
English
Chinese
moss_transcribe_diarize
text2text-generation
moss
audio
speech
asr
diarization
timestamp-asr
long-form-audio
multimodal
multilingual
custom_code
Eval Results
Instructions to use OpenMOSS-Team/MOSS-Transcribe-Diarize with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMOSS-Team/MOSS-Transcribe-Diarize with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("OpenMOSS-Team/MOSS-Transcribe-Diarize", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("OpenMOSS-Team/MOSS-Transcribe-Diarize", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from OpenMOSS-Team/MOSS-Transcribe-Diarize: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize/resolve/main/tokenizer.json
- Command line
-
hf download hf://OpenMOSS-Team/MOSS-Transcribe-Diarize/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 2e66cc2841d06bfaca1dada8c35fc4e0cc78340822aad69f0ff82e699890145f
- Size of remote file:
- 11.4 MB
- SHA256:
- bcf03774334462d6e34b5005cb11120a62275f146ee2953e68731ecdbce84fbb
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