Add LLM and Embedding runner too

README.md

@@ -1,6 +1,6 @@
 # STT Runner
 
-Speech-to-Text transcription using sherpa-onnx + Qwen3-ASR, plus Text-to-Speech with ZipVoice (zero-shot voice cloning).
+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
 
@@ -25,6 +25,20 @@ python tts_runner.py [--output=out.wav] [--ref-audio=ref.wav] [--ref-text="..."]
 
 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/`:
@@ -32,6 +46,8 @@ 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.
 
@@ -56,4 +72,20 @@ wget -qO- https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/she
   | 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"
 ```

config/embed.py

@@ -0,0 +1,12 @@
+from config.model import MODEL_DIR
+
+MODEL = MODEL_DIR / "nomic-embed-text-v1.5" / "nomic-embed-text-v1.5.Q4_K_M.gguf"
+
+N_THREADS = 10
+N_CTX     = 2048  # nomic GGUF defaults to 2048; 8192 needs YaRN scaling
+
+# nomic-embed-text needs a task instruction prefix per text.
+# Use a query prefix for search/user input, a document prefix for stored text.
+PREFIX_QUERY    = "search_query: "
+PREFIX_DOCUMENT = "search_document: "
+DEFAULT_PREFIX  = PREFIX_QUERY

config/llm.py

@@ -0,0 +1,19 @@
+from config.model import MODEL_DIR
+
+MODEL = MODEL_DIR / "granite-4.2" / "granite-4.2-8b-Q4_K_M.gguf"
+
+N_THREADS      = 10
+N_CTX          = 8192  # Context window (tokens)
+N_GPU_LAYERS   = 0     # 0 = full CPU, >0 offload N layers to GPU
+MAX_TOKENS     = 1024  # Max tokens per reply
+STREAM         = True  # Stream tokens as they are generated
+
+TEMPERATURE = 0.7
+TOP_P       = 0.9
+TOP_K       = 40
+
+# System prompt for the chat session.
+SYSTEM_PROMPT = "You are a helpful, concise assistant."
+
+# Default chat template. Leave empty to use the template embedded in the GGUF.
+CHAT_TEMPLATE = ""

core/embed_room.py

@@ -0,0 +1,5 @@
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("texts", nargs="+", help="Text(s) to embed")
+parser.add_argument("--prefix", type=str, default=None, help="Task prefix, overrides config DEFAULT_PREFIX")

core/llm_room.py

@@ -0,0 +1,6 @@
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("prompt",    nargs="?",   default=None, help="Single prompt to answer, then exit. Omit for interactive chat")
+parser.add_argument("--system",  type=str,    default=None, help="Override system prompt from config")
+parser.add_argument("--max-tokens", type=int, default=None, help="Override config MAX_TOKENS")

embed_runner.py

@@ -0,0 +1,37 @@
+import sys
+import json
+from core import embed_room as ap
+from config.embed import MODEL, N_THREADS, N_CTX, DEFAULT_PREFIX
+from llama_cpp import Llama
+
+def embed_run(args):
+
+    if not MODEL.is_file():
+        print(f"Model not found: {MODEL}. Download it first (see README).", file=sys.stderr)
+        return
+
+    prefix = args.prefix if args.prefix is not None else DEFAULT_PREFIX
+
+    print("Loading model...")
+    llm = Llama(
+        model_path = str(MODEL),
+        n_ctx      = N_CTX,
+        n_threads  = N_THREADS,
+        embedding  = True,
+        verbose    = False,
+    )
+    print("Model ready!")
+
+    inputs  = [prefix + t for t in args.texts]
+
+    resp    = llm.create_embedding(inputs)
+    vectors = [d["embedding"] for d in resp["data"]]
+
+    out = [
+        {"text": text, "dim": len(vec), "embedding": vec}
+        for text, vec in zip(args.texts, vectors)
+    ]
+    print(json.dumps(out, ensure_ascii=False, indent=2))
+
+if __name__ == "__main__":
+    embed_run( ap.parser.parse_args() )

llm_runner.py

@@ -0,0 +1,84 @@
+import sys
+from core import llm_room as ap
+from config.llm import (
+    MODEL, N_THREADS, N_CTX, N_GPU_LAYERS, MAX_TOKENS, STREAM,
+    TEMPERATURE, TOP_P, TOP_K, SYSTEM_PROMPT, CHAT_TEMPLATE,
+)
+from llama_cpp import Llama
+
+def load_llm():
+    print("Loading model...")
+    llm = Llama(
+        model_path   = str(MODEL),
+        n_ctx        = N_CTX,
+        n_threads    = N_THREADS,
+        n_gpu_layers = N_GPU_LAYERS,
+        chat_format  = CHAT_TEMPLATE or None,
+        verbose      = False,
+    )
+    print("Model ready!")
+    return llm
+
+def chat_once(llm, messages, max_tokens):
+    if STREAM:
+        text = ""
+        for chunk in llm.create_chat_completion(
+            messages,
+            stream     = True,
+            temperature = TEMPERATURE,
+            top_p      = TOP_P,
+            top_k      = TOP_K,
+            max_tokens = max_tokens,
+        ):
+            delta = chunk["choices"][0]["delta"].get("content")
+            if delta:
+                print(delta, end="", flush=True)
+                text += delta
+        print()
+        return text
+
+    resp = llm.create_chat_completion(
+        messages,
+        stream     = False,
+        temperature = TEMPERATURE,
+        top_p      = TOP_P,
+        top_k      = TOP_K,
+        max_tokens = max_tokens,
+    )
+    return resp["choices"][0]["message"]["content"]
+
+def llm_run(args):
+
+    if not MODEL.is_file():
+        print(f"Model not found: {MODEL}. Download it first (see README).", file=sys.stderr)
+        return
+
+    llm        = load_llm()
+    system     = args.system if args.system is not None else SYSTEM_PROMPT
+    max_tokens = args.max_tokens if args.max_tokens is not None else MAX_TOKENS
+
+    if args.prompt is not None:
+        messages = [{"role": "system", "content": system}] if system else []
+        messages.append({"role": "user", "content": args.prompt})
+        chat_once(llm, messages, max_tokens)
+        return
+
+    messages = [{"role": "system", "content": system}] if system else []
+    print("Interactive chat. Type 'exit' or 'quit' to leave.")
+    try:
+        while True:
+            user = input("\nYou      : ").strip()
+            if user.lower() in ("exit", "quit", "q"):
+                print("Bye.")
+                break
+            if not user:
+                continue
+            messages.append({"role": "user", "content": user})
+            print("Assistant: ", end="", flush=True)
+            reply = chat_once(llm, messages, max_tokens)
+            messages.append({"role": "assistant", "content": reply})
+    except (KeyboardInterrupt, EOFError):
+        print("\nBye.")
+
+if __name__ == "__main__":
+    llm_run( ap.parser.parse_args() )

requirements.txt

@@ -1,2 +1,3 @@
 sherpa-onnx
-soundfile
+soundfile
+llama-cpp-python

usage-embed.sh

@@ -0,0 +1,19 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+BASE_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PYTHON="${PYTHON:-$BASE_DIR/.venv/bin/python}"
+
+if [ ! -x "$PYTHON" ]; then
+    echo "Venv not found. Create it with: python3 -m venv .venv && .venv/bin/pip install -r requirements.txt" >&2
+    exit 1
+fi
+
+if [ $# -lt 1 ]; then
+    echo "Usage: $0 <text...> [--prefix <task prefix>]" >&2
+    echo "       Embed text(s) with nomic-embed-text-v1.5 (llama.cpp)." >&2
+    echo "       Default prefix and model path are set in config/embed.py." >&2
+    exit 1
+fi
+
+"$PYTHON" "$BASE_DIR/embed_runner.py" "$@"

usage-llm.sh

@@ -0,0 +1,12 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+BASE_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PYTHON="${PYTHON:-$BASE_DIR/.venv/bin/python}"
+
+if [ ! -x "$PYTHON" ]; then
+    echo "Venv not found. Create it with: python3 -m venv .venv && .venv/bin/pip install -r requirements.txt" >&2
+    exit 1
+fi
+
+"$PYTHON" "$BASE_DIR/llm_runner.py" "$@"