STT Runner

Speech-to-Text transcription using sherpa-onnx + Qwen3-ASR, plus Text-to-Speech with ZipVoice (zero-shot voice cloning).

Installation

python3 -m venv .venv
.venv/bin/pip install -r requirements.txt

Usage

Speech-to-Text

python stt_runner.py [--language=Indonesian] audio1.wav audio2.wav ...

Text-to-Speech

python tts_runner.py [--output=out.wav] [--ref-audio=ref.wav] [--ref-text="..."] "text to speak"

Output defaults to output.wav. The reference audio/text (voice to clone) is set in config/tts.py and can be overridden per-run with --ref-audio / --ref-text (the text must match the audio exactly).

Configuration

Model paths and inference parameters are hardcoded in config/:

• config/model.py — model paths (conv_frontend, encoder, decoder, tokenizer under models/)
• config/asr.py — inference params: LANGUAGE, HOTWORDS, NUM_THREADS, PROVIDER, SAMPLE_RATE, FEATURE_DIM, MAX_TOTAL_LEN, MAX_NEW_TOKENS
• config/tts.py — TTS model paths, REFERENCE_AUDIO, REFERENCE_TEXT, OUTPUT_FILE, NUM_THREADS, PROVIDER, NUM_STEPS

LANGUAGE defaults to "" (all languages / auto-detect). Passing --language on the CLI overrides it.

Download Model (Qwen3-ASR 1.7B int8)

BASE="https://modelscope.cn/models/zengshuishui/Qwen3-ASR-onnx/resolve/master"
mkdir -p models/model_1.7B models/tokenizer
wget -O models/model_1.7B/conv_frontend.onnx "$BASE/model_1.7B/conv_frontend.onnx"
wget -O models/model_1.7B/encoder.int8.onnx  "$BASE/model_1.7B/encoder.int8.onnx"
wget -O models/model_1.7B/decoder.int8.onnx  "$BASE/model_1.7B/decoder.int8.onnx"
for f in vocab.json merges.txt tokenizer_config.json preprocessor_config.json config.json chat_template.json; do
  wget -O "models/tokenizer/$f" "$BASE/tokenizer/$f"
done

Download Model (ZipVoice TTS)

mkdir -p models/zipvoice
wget -qO- https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/sherpa-onnx-zipvoice-distill-int8-zh-en-emilia.tar.bz2 \
  | tar xjf - -C models/zipvoice --strip-components=1
wget -O models/zipvoice/vocos_24khz.onnx \
  https://github.com/k2-fsa/sherpa-onnx/releases/download/vocoder-models/vocos_24khz.onnx