Instructions to use jiangzhuo9357/pocket-tts-es-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Pocket-TTS
How to use jiangzhuo9357/pocket-tts-es-onnx with Pocket-TTS:
from pocket_tts import TTSModel import scipy.io.wavfile tts_model = TTSModel.load_model("jiangzhuo9357/pocket-tts-es-onnx") voice_state = tts_model.get_state_for_audio_prompt( "hf://kyutai/tts-voices/alba-mackenna/casual.wav" ) audio = tts_model.generate_audio(voice_state, "Hello world, this is a test.") # Audio is a 1D torch tensor containing PCM data. scipy.io.wavfile.write("output.wav", tts_model.sample_rate, audio.numpy()) - Notebooks
- Google Colab
- Kaggle
Pocket TTS ONNX โ Spanish
Flat mirror of the spanish int8 ONNX bundle for Pocket TTS
(Kyutai CALM zero-shot voice-cloning TTS), repackaged for the
Sokuji Local Native provider's standard
model-repo download path. Source bundle: the
KevinAHM/pocket-tts-web Space
(onnx/spanish/), whose export scripts and licensing apply.
Contents: five int8 ONNX graphs (mimi encoder/decoder, flow-LM main/flow, text
conditioner), tokenizer.model, bundle.json, bos_before_voice.npy, voices.bin
(eight predefined voices as flow-LM KV-cache prefixes), and voices/manifest.json
(the voice list Sokuji's UI reads).
Model weights: CC-BY-4.0 (attribution: Kyutai's Pocket TTS; ONNX export by KevinAHM). Do not use these files outside the terms of that license.
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