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
glm-4-voice-decoder-emo-ft
Built with glm-4.
Fine-tuned GLM-4-Voice decoder weights for emotion-preserving Chinese ↔ English speech-to-speech translation, used together with the Kimi-Audio Emotion-Aware S2ST training / inference pipeline.
Files
| File | Size | Role |
|---|---|---|
epoch500_emoft.pt |
~425 MB | Fine-tuned flow checkpoint (emotion-preserving) |
hift.pt |
~79 MB | HiFT vocoder checkpoint |
Usage
git clone https://github.com/<YOUR_GH_USER>/kimi-audio-release
cd kimi-audio-release
./scripts/download_weights.sh
# the two files will be placed under glm_4_voice_decoder/
'EOF'
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