init commit

app.py

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+import gradio as gr
+import subprocess, json, os, io, tempfile
+from faster_whisper import WhisperModel
+from ollama import Client as OllamaClient
+
+# ---- CONFIG ----
+LLM_MODEL = "llama3.2:3b"      # or "mistral:7b", "qwen2.5:3b"
+WHISPER_SIZE = "small"         # "base", "small", "medium"
+USE_SILERO = True              # set False to use Coqui XTTS v2
+USE_CONTEXT = False  # <— new: disable conversational memory
+
+import os
+USE_REMOTE_OLLAMA = bool(os.getenv("OLLAMA_HOST"))
+
+
+if not USE_REMOTE_OLLAMA:
+    # Transformers fallback for Spaces (CPU-friendly small instruct model)
+    from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+    HF_CHAT_MODEL = os.getenv("HF_CHAT_MODEL", "google/gemma-2-2b-it")  # small instruct model that runs on CPU
+    HF_TOKEN = os.getenv("HF_TOKEN")
+    
+    _tok = AutoTokenizer.from_pretrained(HF_CHAT_MODEL, token=HF_TOKEN)
+    _mdl = AutoModelForCausalLM.from_pretrained(HF_CHAT_MODEL, token=HF_TOKEN, torch_dtype="auto", device_map="auto")
+    gen = pipeline("text-generation", model=_mdl, tokenizer=_tok, max_new_tokens=256)
+
+
+
+# ---- STT (faster-whisper) ----
+# Run on GPU if available: compute_type="float16", device="cuda"
+stt_model = WhisperModel(WHISPER_SIZE, device="cuda" if os.environ.get("CUDA_VISIBLE_DEVICES") else "cpu",
+                         compute_type="float16" if os.environ.get("CUDA_VISIBLE_DEVICES") else "int8")
+
+def speech_to_text(audio_path: str) -> str:
+    segments, info = stt_model.transcribe(audio_path, beam_size=1, vad_filter=True, language="en")
+    text = "".join(seg.text for seg in segments).strip()
+    return text
+
+# ---- LLM (Ollama) ----
+# ollama = OllamaClient(host="http://127.0.0.1:11434")
+
+SYSTEM_PROMPT = """You are a friendly AI voice assistant.
+Reply in one short, natural sentence only.
+Sound warm and conversational, never formal.
+Avoid multi-sentence or paragraph answers."""
+
+def chat_with_llm(history_messages, user_text):
+    if USE_REMOTE_OLLAMA:
+        # Only system + current user
+        messages = [
+            {"role": "system", "content": SYSTEM_PROMPT},
+            {"role": "user", "content": user_text},
+        ]
+        resp = ollama.chat(model=LLM_MODEL, messages=messages)
+        return resp["message"]["content"]
+    else:
+        # Only system + current user
+        prompt = f"{SYSTEM_PROMPT}\nUser: {user_text}\nAssistant:"
+        out = gen(prompt, return_full_text=False, max_new_tokens=25, temperature=0.8, repetition_penalty=1.1,)[0]["generated_text"].split("\n")[0].strip()
+        return out
+
+
+
+
+
+
+
+
+
+# near top-level (global singletons)
+_SILERO_TTS = None
+
+def tts_silero(text: str) -> str:
+    """
+    Return path to WAV synthesized by Silero TTS.
+    Uses a cached model instance to avoid re-downloading each request.
+    """
+    import torch, tempfile
+    import soundfile as sf
+
+    global _SILERO_TTS
+    if _SILERO_TTS is None:
+        obj = torch.hub.load(
+            repo_or_dir="snakers4/silero-models",
+            model="silero_tts",
+            language="en",
+            speaker="v3_en",
+            trust_repo=True,   # avoids interactive trust prompt
+        )
+        _SILERO_TTS = obj[0] if isinstance(obj, (list, tuple)) else obj
+
+    model = _SILERO_TTS
+    sample_rate = 48000
+    speaker = "en_0"
+    audio = model.apply_tts(text=text, speaker=speaker, sample_rate=sample_rate)
+
+    out_wav = tempfile.mktemp(suffix=".wav")
+    sf.write(out_wav, audio, sample_rate)
+    return out_wav
+
+
+
+def tts_coqui_xtts(text: str) -> str:
+    """
+    Returns path to a WAV file synthesized by Coqui XTTS v2 (higher quality; GPU-friendly).
+    """
+    from TTS.api import TTS
+    tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2")
+    out_wav = tempfile.mktemp(suffix=".wav")
+    tts.tts_to_file(text=text, file_path=out_wav, speaker="female-en-5", language="en")
+    return out_wav
+
+def text_to_speech(text: str) -> str:
+    if USE_SILERO:
+        return tts_silero(text)
+    else:
+        return tts_coqui_xtts(text)
+
+# ---- Gradio pipeline ----
+def pipeline(audio, history):
+    # audio is (sample_rate, np.array) OR a filepath (depends on Gradio version)
+    # Normalize to a temp wav file
+    if audio is None:
+        return history, None, "Please speak something."
+
+    if isinstance(audio, tuple):
+        # (sr, data) -> write wav
+        import soundfile as sf, numpy as np, tempfile
+        sr, data = audio
+        tmp_in = tempfile.mktemp(suffix=".wav")
+        sf.write(tmp_in, data.astype("float32"), sr)
+        audio_path = tmp_in
+    else:
+        audio_path = audio  # path already
+
+    user_text = speech_to_text(audio_path)
+    if not user_text:
+        return history, None, "Didn't catch that—could you repeat?"
+
+    reply = chat_with_llm(history, user_text)
+
+    # Extract the "Reply:" line for TTS; speak only the conversational reply
+    speak_text = reply
+    for tag in ["Reply:", "Correction:", "Why:"]:
+        # Try to find "Reply:" block
+        if "Reply:" in reply:
+            speak_text = reply.split("Reply:", 1)[1].strip()
+            break
+
+    wav_path = text_to_speech(speak_text)
+    updated = (history or []) + [
+        {"role": "user", "content": user_text},
+        {"role": "assistant", "content": reply},
+    ]
+    return updated, wav_path, ""
+
+with gr.Blocks(title="Voice Coach") as demo:
+    gr.Markdown("## 🎙️ Interactive Voice Chat")
+    with gr.Row():
+        audio_in = gr.Audio(sources=["microphone"], type="filepath", label="Speak")
+        audio_out = gr.Audio(label="Assistant (TTS)", autoplay=True)
+    chatbox = gr.Chatbot(type="messages", height=300)
+    status = gr.Markdown()
+    btn = gr.Button("Send")
+
+    # Use continuous recording or press "Send" after recording
+    audio_in.change(pipeline, inputs=[audio_in, chatbox], outputs=[chatbox, audio_out, status])
+    btn.click(pipeline, inputs=[audio_in, chatbox], outputs=[chatbox, audio_out, status])
+
+if __name__ == "__main__":
+    demo.launch()

apt.txt

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+ffmpeg

requirements.txt

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+# --- Core app / UI ---
+gradio==5.49.1
+gradio-client==1.13.3
+huggingface_hub==0.33.5    # NOTE: <1.0 to satisfy transformers, >=0.33.5 to satisfy gradio
+
+# --- STT ---
+faster-whisper==1.2.0
+ctranslate2==4.6.0
+onnxruntime==1.23.2
+
+# --- LLM fallback for Spaces (since no local Ollama) ---
+transformers==4.44.2
+accelerate
+safetensors
+sentencepiece
+einops
+protobuf==3.20.3          # keeps old protobuf constraint friendly to py3.10 wheels
+
+# --- TTS & audio IO ---
+torch==2.4.0              # CPU build; OK on Spaces' CPU machines
+soundfile==0.13.1
+pydub==0.25.1
+numpy==1.26.4             # last NumPy supporting Python 3.10
+
+# --- HTTP server ---
+uvicorn==0.38.0
+
+ollama
+
+omegaconf==2.3.0
+num2words==0.5.13

runtime.txt

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+python-3.11