| |
| |
| |
| try: |
| import spaces |
| except ImportError: |
| class spaces: |
| @staticmethod |
| def GPU(fn): |
| return fn |
|
|
| import os |
| import uuid |
| import whisper |
| import gradio as gr |
| import soundfile as sf |
| import numpy as np |
| import scipy.io.wavfile as wav |
| from groq import Groq |
| from f5_tts.api import F5TTS |
|
|
| |
| |
| |
| GROQ_API_KEY = os.environ.get("GROQ_API_KEY", "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") |
| voice_file = "my_voice_fixed.wav" |
|
|
| if not os.path.exists(voice_file): |
| print(f"⚠️ Reference audio file '{voice_file}' not found! Creating safe baseline array profile...") |
| sample_rate = 24000 |
| duration = 1.0 |
| audio_data = np.zeros(int(sample_rate * duration), dtype=np.int16) |
| wav.write(voice_file, sample_rate, audio_data) |
|
|
| groq_client = Groq(api_key=GROQ_API_KEY) if GROQ_API_KEY else None |
| whisper_model = None |
| f5tts = None |
| auto_reference_text = None |
|
|
| |
| |
| |
| def get_ai_answer(question): |
| global groq_client |
| if not GROQ_API_KEY: |
| return "Please add your active GROQ_API_KEY into Hugging Face Settings -> Secrets to chat." |
| |
| try: |
| if groq_client is None: |
| groq_client = Groq(api_key=GROQ_API_KEY) |
| |
| chat_completion = groq_client.chat.completions.create( |
| messages=[ |
| { |
| "role": "system", |
| "content": "You are an informative voice simulation assistant. Provide comprehensive, detailed, and clear explanations. Avoid single-sentence answers." |
| }, |
| {"role": "user", "content": question} |
| ], |
| model="llama-3.3-70b-versatile", |
| max_tokens=250, |
| timeout=15 |
| ) |
| return chat_completion.choices[0].message.content.strip() |
| except Exception as e: |
| print(f"⚠️ Groq API issue detected: {e}") |
| return f"The AI engine is currently offline. Details: {str(e)}" |
|
|
| |
| |
| |
| def clone_and_speak(text): |
| global f5tts, auto_reference_text |
| try: |
| if "offline" in text.lower() or "secrets" in text.lower(): |
| return None |
| |
| unique_filename = f"voice_output_{uuid.uuid4().hex[:8]}.wav" |
| print(f"🔊 Rendering voice cloning track for text: '{text}'") |
| |
| if f5tts is None: |
| print("Loading F5-TTS Core Neural Simulator...") |
| f5tts = F5TTS() |
| |
| if auto_reference_text is None: |
| auto_reference_text = "Hi, my name is Rabbani. I am software engineering in the Gamana solution." |
| |
| result = f5tts.infer( |
| ref_file=voice_file, |
| ref_text=auto_reference_text, |
| gen_text=text |
| ) |
| |
| if isinstance(result, tuple): |
| if len(result) == 2: |
| audio_data, sample_rate = result[0], result[1] |
| elif len(result) >= 3: |
| audio_data, sample_rate = result[0], result[1] |
| else: |
| audio_data, sample_rate = result[0], 24000 |
| else: |
| audio_data, sample_rate = result, 24000 |
| |
| if hasattr(audio_data, "cpu"): |
| audio_data = audio_data.cpu().numpy() |
| elif hasattr(audio_data, "numpy"): |
| audio_data = audio_data.numpy() |
| |
| audio_data = np.squeeze(audio_data) |
| sf.write(unique_filename, audio_data, sample_rate, format='WAV') |
| |
| if os.path.exists(unique_filename) and os.path.getsize(unique_filename) > 0: |
| print(f"🎉 Generated track successfully saved to: {unique_filename}") |
| return unique_filename |
| return None |
| except Exception as e: |
| print(f"❌ Core TTS System Exception triggered: {e}") |
| return None |
|
|
| |
| |
| |
| @spaces.GPU |
| def chatbot_engine(text_input, audio_input): |
| global whisper_model, auto_reference_text |
| if whisper_model is None: |
| print("Loading Lightweight Whisper Engine...") |
| whisper_model = whisper.load_model("tiny") |
| |
| if auto_reference_text is None: |
| try: |
| ref_result = whisper_model.transcribe(voice_file, fp16=False) |
| auto_reference_text = ref_result["text"].strip() |
| if not auto_reference_text: |
| auto_reference_text = "Hi, my name is Rabbani. I am software engineering in the Gamana solution." |
| except Exception: |
| auto_reference_text = "Hi, my name is Rabbani. I am software engineering in the Gamana solution." |
| |
| if audio_input is not None: |
| user_transcription = whisper_model.transcribe(audio_input, fp16=False) |
| question = user_transcription["text"] |
| elif text_input and text_input.strip() != "": |
| question = text_input |
| else: |
| return "System Notification: Please enter a text question or record your microphone.", None |
| |
| ai_text_response = get_ai_answer(question) |
| generated_audio_track = clone_and_speak(ai_text_response) |
| return ai_text_response, generated_audio_track, gr.update(value=""), gr.update(value=None) |
|
|
| |
| |
| |
| with gr.Blocks(title="Rabbani A I voice clone") as interface: |
| gr.Markdown("# 🎙️ Rabbani A I voice clone") |
| gr.Markdown("Type or speak your question below. The system processes the response dynamically and outputs both text and voice simulation results simultaneously.") |
| |
| with gr.Row(): |
| with gr.Column(scale=1): |
| gr.Markdown("### 💬 Input Interaction Canvas") |
| text_prompt_box = gr.Textbox(placeholder="Type your custom question query here...", label="Your Written Question") |
| audio_prompt_box = gr.Audio(label="Or Record/Speak Your Question Directly", type="filepath") |
| submit_btn = gr.Button("Send Question to AI Engine", variant="primary") |
| clear_btn = gr.ClearButton(value="Clear Inputs") |
| |
| with gr.Column(scale=1): |
| gr.Markdown("### 📊 Cloned Output Responses") |
| text_output_box = gr.Textbox(label="AI Answer Text Output", interactive=False) |
| audio_output_player = gr.Audio( |
| label="Rendered Cloned Response Audio Player", |
| type="filepath", |
| interactive=False |
| ) |
| |
| submit_btn.click( |
| fn=chatbot_engine, |
| inputs=[text_prompt_box, audio_prompt_box], |
| outputs=[text_output_box, audio_output_player, text_prompt_box, audio_prompt_box] |
| ) |
| clear_btn.add([text_prompt_box, audio_prompt_box, text_output_box, audio_output_player]) |
|
|
| interface.queue().launch() |
|
|
