Automatic Speech Recognition
Transformers
Safetensors
cohere_asr
audio
speech-recognition
transcription
diarization
speaker-diarization
timestamps
custom_code
Instructions to use syvai/cohere-transcribe-diarize with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use syvai/cohere-transcribe-diarize with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="syvai/cohere-transcribe-diarize", trust_remote_code=True)# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("syvai/cohere-transcribe-diarize", trust_remote_code=True) model = AutoModelForSpeechSeq2Seq.from_pretrained("syvai/cohere-transcribe-diarize", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Refit on cased LibriSpeech-PC + AMI XML transcripts
Browse files- model.safetensors +1 -1
- tokenizer_config.json +1 -1
model.safetensors
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tokenizer_config.json
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"pad_token": "<pad>",
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"sp_model_kwargs": {},
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