have it working it as a bloack

=2.0

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-

app.py

@@ -1,239 +1,45 @@
-# app.py
-# Minimal Gradio voice-assistant demo for Hugging Face Spaces.
-# - STT: faster-whisper (WhisperModel)
-# - Sentiment: transformers pipeline
-# - LLM: local HF model (small) or HF Inference API (recommended for huge models)
-# - TTS: bosonai/higgs-audio-v2 (when available)
-#
-# NOTES:
-# - Replace model names with versions you prefer.
-# - If you want streaming / low-latency WebRTC, see the "Advanced: WebRTC / streaming" comments below.
-
-import os
-import tempfile
-import uuid
-from pathlib import Path
-from faster_whisper import WhisperModel
-from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
-import torch
 import gradio as gr
-from huggingface_hub import InferenceApi, hf_hub_download
-
-# --------------------
-# CONFIG / ENV VARS
-# --------------------
-HUGGINGFACEHUB_API_TOKEN = os.environ.get("HUGGINGFACEHUB_API_TOKEN")  # optional for hosted inference
-USE_HOSTED_LLM = bool(os.environ.get("USE_HOSTED_LLM", ""))  # set to "1" to use hosted HF inference instead of a local model
-LLM_MODEL_NAME = os.environ.get("LLM_MODEL_NAME", "gpt2")  # default small model for Spaces demo
-TTS_MODEL = os.environ.get("TTS_MODEL", "bosonai/higgs-audio-v2-generation-3B-base")  # example; may need HF token
-DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-
-# --------------------
-# Initialize models
-# --------------------
-
-# Faster Whisper STT
-# Choose model size: "small", "medium", "large-v2", etc. Smaller -> lower latency.
-whisper_model_size = "small"
-whisper = WhisperModel(whisper_model_size, device=DEVICE, compute_type="int8_float16")  # compute_type optional based on your hardware
-
-# Sentiment / conversation cue analysis
-sentiment = pipeline("sentiment-analysis", device=0 if DEVICE == "cuda" else -1)
-
-# LLM (local fallback)
-local_llm = None
-local_tokenizer = None
-if not USE_HOSTED_LLM:
-    try:
-        # Keep this small on Spaces by default. Swap to a bigger model if you have the hardware.
-        local_llm_name = LLM_MODEL_NAME  # e.g., "gpt2", "EleutherAI/gpt-neo-125M", or an HF chat model
-        local_tokenizer = AutoTokenizer.from_pretrained(local_llm_name)
-        local_llm = AutoModelForCausalLM.from_pretrained(local_llm_name, device_map="auto" if DEVICE=="cuda" else None)
-    except Exception as e:
-        print("Failed to load local LLM:", e)
-        local_llm = None
-
-# Hosted inference (optional)
-hf_inference = None
-if USE_HOSTED_LLM and HUGGINGFACEHUB_API_TOKEN:
-    hf_inference = InferenceApi(repo_id=os.environ.get("HF_INFERENCE_MODEL", "openai/gpt-oss-7b"), token=HUGGINGFACEHUB_API_TOKEN)
-
-
-# TTS: This is an illustration. For actual use, replace with a tested TTS pipeline suited to the model.
-def tts_generate(text: str, out_path: str) -> str:
-    """
-    Generate TTS audio for `text` and save to out_path.
-    For Higgs Audio v2 (BosonAI) you likely need a specific inference client or HF pipeline that supports that repo.
-    This function is a stub illustrating where TTS would be called.
-    """
-    # Option A: If the TTS repo provides a pipeline via transformers (pseudo):
-    # from transformers import pipeline
-    # tts_pipe = pipeline("text-to-speech", model=TTS_MODEL, use_auth_token=HUGGINGFACEHUB_API_TOKEN)
-    # audio_array, sr = tts_pipe(text)
-    # save audio to out_path here...
-    #
-    # Option B: If using hosted HF inference:
-    if HUGGINGFACEHUB_API_TOKEN and os.environ.get("USE_HOSTED_TTS"):
-        # Using HF Inference API (simplified)
-        from huggingface_hub import InferenceApi
-        inf = InferenceApi(repo_id=TTS_MODEL, token=HUGGINGFACEHUB_API_TOKEN)
-        res = inf(text, {"wait_for_model": True})
-        # hf returns audio in base64 or a URL depending on model; handle accordingly (pseudo)
-        # Save binary to out_path...
-    # Fallback: return an empty audio or a short beep (silence)
-    # For demo, create a tiny silent WAV so Gradio can play something:
-    import wave, struct
-    with wave.open(out_path, "wb") as wf:
-        n_channels = 1
-        sampwidth = 2
-        framerate = 16000
-        n_frames = framerate // 10  # 0.1s silence
-        wf.setnchannels(n_channels)
-        wf.setsampwidth(sampwidth)
-        wf.setframerate(framerate)
-        wf.writeframes(b"\x00\x00" * n_frames)
-    return out_path
-
-# --------------------
-# Core pipeline functions
-# --------------------
-
-def transcribe_audio(audio_file: str):
-    """
-    audio_file: path to uploaded audio (wav/m4a/ogg)
-    returns: transcription string and whisper segments (list)
-    """
-    segments = []
-    text_acc = []
-    # faster-whisper: transcribe file
-    # model.transcribe returns generator of segments when streaming; we call full transcription here
-    try:
-        result = whisper.transcribe(audio_file, beam_size=5, word_timestamps=False)
-        # result is a list of segments or object depending on version. We'll handle simple case:
-        # If result has 'segments' attribute (older/variant) handle it:
-        if hasattr(result, "segments"):
-            for seg in result.segments:
-                segments.append({"start": seg.start, "end": seg.end, "text": seg.text})
-                text_acc.append(seg.text)
-        else:
-            # result may be tuple/text depending on faster-whisper version
-            if isinstance(result, dict) and "text" in result:
-                text_acc.append(result["text"])
-            elif isinstance(result, str):
-                text_acc.append(result)
-    except Exception as e:
-        print("STT error:", e)
-        return "",""
+import numpy as np
+from faster_whisper import WhisperModel
 
-    transcription = " ".join(text_acc).strip()
-    return transcription, segments
+model = WhisperModel("tiny.en")
 
-def analyze_sentiment(text: str):
-    if not text:
-        return {"label":"NEUTRAL", "score": 0.0}
-    try:
-        out = sentiment(text[:1000])  # limit to first 1000 chars
-        return out[0] if isinstance(out, list) and out else out
-    except Exception as e:
-        print("Sentiment error:", e)
-        return {"label":"UNKNOWN", "score": 0.0}
+def normaliseData(audioInput, stream):
+    sr, y = audioInput
+    
+    # Convert to mono if stereo
+    if y.ndim > 1:
+        y = y.mean(axis=1)
+        
+    y = y.astype(np.float32)
+    y /= np.max(np.abs(y))
 
-def llm_respond(prompt: str, conversation_history=None, max_length=256):
-    """
-    Returns text response for prompt. Uses hosted HF inference if USE_HOSTED_LLM else local model.
-    conversation_history: list of (user, assistant) tuples (not deeply used in demo)
-    """
-    if USE_HOSTED_LLM and hf_inference:
-        # Use HF Inference API (simplified)
-        payload = {"inputs": prompt}
-        try:
-            result = hf_inference(prompt)
-            # result could be a dict with 'generated_text' or a string
-            if isinstance(result, dict) and "generated_text" in result:
-                return result["generated_text"]
-            elif isinstance(result, str):
-                return result
-            else:
-                return str(result)
-        except Exception as e:
-            print("Hosted LLM error:", e)
-            return "Sorry — I couldn't reach the hosted model."
-    elif local_llm and local_tokenizer:
-        # Local generation (small models suitable for Spaces)
-        inputs = local_tokenizer(prompt, return_tensors="pt").to(DEVICE)
-        outputs = local_llm.generate(**inputs, max_new_tokens=max_length)
-        return local_tokenizer.decode(outputs[0], skip_special_tokens=True)
+    if stream is not None:
+        stream = np.concatenate([stream, y])
     else:
-        return "No LLM available. Set USE_HOSTED_LLM=1 and provide a HUGGINGFACEHUB_API_TOKEN or load a local model."
+        stream = y
+    
+    # Return the stream as state and a string representation of the array for display
+    return stream, str(stream)
 
-# --------------------
-# Gradio UI callbacks
-# --------------------
 
-def process_audio_and_respond(audio):
-    """
-    audio: a tuple or path from gradio Audio component
-    Returns: transcription, sentiment label, assistant text, tts_audio_path
-    """
-    if audio is None:
-        return "","", "No audio received.", None
-    # gradio audio file can be a dict or tuple; handle both
-    if isinstance(audio, tuple) or isinstance(audio, list):
-        # (sample_rate, numpy_array) OR (path, sample_rate)
-        # gradio sends a path when type="filepath"
-        audio_path = audio[0] if isinstance(audio[0], str) else None
-    elif isinstance(audio, str) and Path(audio).exists():
-        audio_path = audio
-    else:
-        # try to handle (np_array, sr)
-        import soundfile as sf
-        tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
-        sf.write(tmp.name, audio[0], audio[1])
-        audio_path = tmp.name
-
-    transcription, segments = transcribe_audio(audio_path)
-    sentiment_res = analyze_sentiment(transcription)
-    sentiment_label = sentiment_res.get("label", "UNKNOWN")
-    # Build a prompt for LLM
-    prompt = f"User: {transcription}\n\nAssistant:"
-    assistant_text = llm_respond(prompt)
+with gr.Blocks() as demo:
+    audioInput = gr.Audio(sources=["microphone"], streaming=True)
+    audioOutput = gr.Textbox(label="Output")
+    state = gr.State()
 
-    # Synthesize TTS (non-blocking improvement: spawn background job)
-    out_audio_path = f"/tmp/assistant_tts_{uuid.uuid4().hex}.wav"
-    tts_generate(assistant_text, out_audio_path)
+    audioInput.stream(
+        fn=normaliseData,
+        inputs=[audioInput, state],
+        outputs=[state, audioOutput] # try switching it arround
+    )
+demo.launch()
 
-    return transcription, sentiment_label, assistant_text, out_audio_path
 
-# --------------------
-# GRADIO APP
-# --------------------
-with gr.Blocks(title="Voice Assistant (Faster Whisper + LLM + Higgs Audio)") as demo:
-    gr.Markdown("# Voice Assistant demo")
-    with gr.Row():
-        with gr.Column(scale=2):
-            audio_in = gr.Audio(source="microphone", type="filepath", label="Speak (record)", show_label=True)
-            btn = gr.Button("Send")
-            status = gr.Label("Status: idle")
-        with gr.Column(scale=3):
-            transcript_out = gr.Textbox(label="Transcription", interactive=False)
-            sentiment_out = gr.Textbox(label="Sentiment", interactive=False)
-            assistant_out = gr.Textbox(label="Assistant response", interactive=False)
-            audio_out = gr.Audio(label="Assistant voice", interactive=False)
 
-    def on_click(audio):
-        status.value = "Processing..."
-        transcription, sentiment_label, assistant_text, tts_path = process_audio_and_respond(audio)
-        status.value = "Done"
-        return gr.update(value=transcription), gr.update(value=sentiment_label), gr.update(value=assistant_text), gr.update(value=tts_path)
 
-    btn.click(on_click, inputs=[audio_in], outputs=[transcript_out, sentiment_out, assistant_out, audio_out])
 
-    gr.Markdown(
-        "### Notes\n"
-        "- This demo uses Faster Whisper for STT, a transformers sentiment classifier, and a small LLM by default.\n"
-        "- Set `USE_HOSTED_LLM=1` & provide `HUGGINGFACEHUB_API_TOKEN` to use a hosted LLM (e.g. gpt-oss variants) via the HF Inference API.\n"
-        "- To enable richer TTS (Higgs Audio v2), set `USE_HOSTED_TTS=1` and make sure your HF token has access to the model if it's gated.\n"
-    )
+segments, info = model.transcribe("audio.mp3")
+for segment in segments:
+    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
 
-if __name__ == '__main__':
-    demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))

working.py

@@ -1,28 +1,28 @@
-# transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
+transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
 
-# def transcribe(stream, new_chunk):
-#     sr, y = new_chunk
+def transcribe(stream, new_chunk):
+    sr, y = new_chunk
     
-#     # Convert to mono if stereo
-#     if y.ndim > 1:
-#         y = y.mean(axis=1)
+    # Convert to mono if stereo
+    if y.ndim > 1:
+        y = y.mean(axis=1)
         
-#     y = y.astype(np.float32)
-#     y /= np.max(np.abs(y))
+    y = y.astype(np.float32)
+    y /= np.max(np.abs(y))
 
-#     if stream is not None:
-#         stream = np.concatenate([stream, y])
-#     else:
-#         stream = y
+    if stream is not None:
+        stream = np.concatenate([stream, y])
+    else:
+        stream = y
     
-#     # Return the stream as state and a string representation of the array for display
-#     return stream, str(stream)
+    # Return the stream as state and a string representation of the array for display
+    return stream, str(stream)
 
-# demo = gr.Interface(
-#     transcribe,
-#     ["state", gr.Audio(sources=["microphone"], streaming=True)],
-#     ["state", "text"],
-#     live=True,
-# )
+demo = gr.Interface(
+    transcribe,
+    ["state", gr.Audio(sources=["microphone"], streaming=True)],
+    ["state", "text"],
+    live=True,
+)
 
-# demo.launch()
+demo.launch()