Instructions to use 47z/glm-4-voice-decoder-emo-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KimiAudio
How to use 47z/glm-4-voice-decoder-emo-ft with KimiAudio:
# Example usage for KimiAudio # pip install git+https://github.com/MoonshotAI/Kimi-Audio.git from kimia_infer.api.kimia import KimiAudio model = KimiAudio(model_path="47z/glm-4-voice-decoder-emo-ft", load_detokenizer=True) sampling_params = { "audio_temperature": 0.8, "audio_top_k": 10, "text_temperature": 0.0, "text_top_k": 5, } # For ASR asr_audio = "asr_example.wav" messages_asr = [ {"role": "user", "message_type": "text", "content": "Please transcribe the following audio:"}, {"role": "user", "message_type": "audio", "content": asr_audio} ] _, text = model.generate(messages_asr, **sampling_params, output_type="text") print(text) # For Q&A qa_audio = "qa_example.wav" messages_conv = [{"role": "user", "message_type": "audio", "content": qa_audio}] wav, text = model.generate(messages_conv, **sampling_params, output_type="both") sf.write("output_audio.wav", wav.cpu().view(-1).numpy(), 24000) print(text) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0803a63692386814d3b8a0987a10039a2978b0ae1c29b60aedcb611ae9170763
- Size of remote file:
- 81.9 MB
- SHA256:
- 91e679b6ca1eff71187ffb4f3ab0444935594cdcc20a9bd12afad111ef8d6012
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