# STT Runner

Speech-to-Text transcription using sherpa-onnx + Qwen3-ASR, Text-to-Speech with ZipVoice (zero-shot voice cloning), and LLM/embedding inference with llama.cpp (Granite-4.2 + nomic-embed-text-v1.5).

## Installation

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

## Usage

### Speech-to-Text

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

### Text-to-Speech

```bash
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).

### LLM Chat (llama.cpp)

```bash
python llm_runner.py "What is the capital of France?"   # single prompt
python llm_runner.py                                     # interactive chat
```

### Embedding (llama.cpp)

```bash
python embed_runner.py "text to embed" "another text"
python embed_runner.py "What is TSNE?" --prefix "search_query: "
```

## 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`
- `config/llm.py` — Granite-4.2 model path, `N_CTX`, `N_THREADS`, `N_GPU_LAYERS`, `MAX_TOKENS`, `TEMPERATURE`, `TOP_P`, `TOP_K`, `SYSTEM_PROMPT`, `CHAT_TEMPLATE`
- `config/embed.py` — nomic-embed-text-v1.5 model path, `N_CTX`, `N_THREADS`, `PREFIX_QUERY`, `PREFIX_DOCUMENT`, `DEFAULT_PREFIX`

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

## Download Model (Qwen3-ASR 1.7B int8)

```bash
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)

```bash
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
```

## Download Model (Granite-4.2 8B Q4_K_M)

```bash
mkdir -p models/granite-4.2
wget -O models/granite-4.2/granite-4.2-8b-Q4_K_M.gguf \
  "https://huggingface.co/ibm-granite/granite-4.2-8b-GGUF/resolve/main/granite-4.2-8b-Q4_K_M.gguf"
```

## Download Model (nomic-embed-text-v1.5)

```bash
mkdir -p models/nomic-embed-text-v1.5
wget -O models/nomic-embed-text-v1.5/nomic-embed-text-v1.5.Q4_K_M.gguf \
  "https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/resolve/main/nomic-embed-text-v1.5.Q4_K_M.gguf"
```