Instructions to use lordnyson/playback-recordings-voices with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Pocket-TTS
How to use lordnyson/playback-recordings-voices with Pocket-TTS:
from pocket_tts import TTSModel import scipy.io.wavfile tts_model = TTSModel.load_model("lordnyson/playback-recordings-voices") 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
Playback Recordings voices
Voice model packs downloaded on first use by Playback Recordings, an offline Android narration app. Each pack is one uncompressed tar that runs through sherpa-onnx on the device.
| Pack | Voices in the app | Size |
|---|---|---|
kokoro-v1.0.tar |
Heart, Michael, Bella | ~360 MB |
pocket-2026-09.tar |
Eponine, Peter Yearsley | ~160 MB |
Nothing here is new model training: these are existing open models, converted
or patched so that sherpa-onnx renders them the way their reference runtimes do.
tools/ holds the scripts that made every changed file.
kokoro-v1.0
model.onnx- Kokoro-82M v1.0 (Apache-2.0, hexgrad), the fp32 ONNX export from thewh1teagle/kokoro-onnx (MIT), with sherpa-onnx's model metadata copied onto it (tools/patch_kokoro_metadata.py). sherpa-onnx's own Kokoro export rebuilds the vocoder and sounds audibly duller (about 9 dB less energy at 4-8 kHz); this keeps the original export's sound.voices.bin,tokens.txt,lexicon-us-en.txt- from sherpa-onnx'skokoro-multi-lang-v1_0release (Apache-2.0). Speaker ids:af_bella2,af_heart3,am_michael16.espeak-ng-data/- eSpeak NG phoneme data, GPL-3.0-or-later; source at that link.
pocket-2026-09
Pocket TTS by
Kyutai, english_2026-09 (6 layers), licensed
CC-BY-4.0. Exported to ONNX with
KevinAHM/pocket-tts-onnx-export
(Apache-2.0) using tools/english_2026-09.yaml, then:
encoder.onnxhas Pocket's learned BOS-before-voice embedding prepended to its output inside the graph (tools/bake_bos.py). Models fromenglish_2026-04on expect it and sherpa-onnx (written forenglish_2026-01) does not insert it; without it the output is noise.vocab.json,token_scores.json- the SentencePiece tokenizer converted with sherpa-onnx'sscripts/pocket-tts/convert_tokenizer.py.lm_main,lm_flowanddecoderare INT8 (dynamic quantization);encoderandtext_conditionerare fp32.voices/- the reference recordings each voice is cloned from, resampled to 24 kHz mono:eponine.wav- VCTK speaker p262, sentence 23, enhanced by Kyutai (kyutai/tts-voices). From the CSTR VCTK Corpus, University of Edinburgh, CC-BY-4.0.peter_yearsley.wav- from Voice-Zero, LibriVox recordings, CC0.
Both are the recordings behind Pocket TTS's own preset voices of those names.
Prohibited use (from Kyutai)
Use of our model must comply with all applicable laws and regulations and must not result in, involve, or facilitate any illegal, harmful, deceptive, fraudulent, or unauthorized activity. Prohibited uses include, without limitation, voice impersonation or cloning without explicit and lawful consent; misinformation, disinformation, or deception (including fake news, fraudulent calls, or presenting generated content as genuine recordings of real people or events); and the generation of unlawful, harmful, libelous, abusive, harassing, discriminatory, hateful, or privacy-invasive content.
Checks
Every pack was rendered through sherpa-onnx and transcribed with Whisper
(base.en); word error rates matched the reference runtimes (kokoro-onnx,
pocket-tts) on the same sentences, and the Kokoro output matched the original
export's spectrum.
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