Audio-Text-to-Text
Transformers
PyTorch
English
Chinese
moss_transcribe_diarize
text-generation
moss
audio
speech
asr
diarization
timestamp-asr
long-form-audio
multimodal
multilingual
custom_code
Instructions to use xqiuresearch/MOSS-Transcribe-Diarize with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xqiuresearch/MOSS-Transcribe-Diarize with Transformers:
# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("xqiuresearch/MOSS-Transcribe-Diarize", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0da527f591afe4333e37c39a1111408286fc622446cfa24df7981288af3c6036
- Size of remote file:
- 1.26 GB
- SHA256:
- 2e319a3e1b69f297d333adee96e441bbed666a0e5e7d3339dc64bf8a0b6cd9ad
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