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| import gradio as gr | |
| from asr import transcribe_auto | |
| from huggingface_hub import InferenceClient | |
| from ttsmms import download, TTS | |
| from langdetect import detect | |
| # Initialize text generation client | |
| client = InferenceClient("Futuresony/future_ai_12_10_2024.gguf") | |
| # Download and load TTS models for Swahili and English | |
| swahili_dir = download("swh", "./data/swahili") | |
| english_dir = download("eng", "./data/english") # Ensure an English TTS model is available | |
| swahili_tts = TTS(swahili_dir) | |
| english_tts = TTS(english_dir) | |
| def is_uncertain(question, response): | |
| """Check if the model's response is unreliable.""" | |
| if len(response.split()) < 4 or response.lower() in question.lower(): | |
| return True | |
| uncertain_phrases = ["Kulingana na utafiti", "Inaaminika kuwa", "Ninadhani", "It is believed that", "Some people say"] | |
| return any(phrase.lower() in response.lower() for phrase in uncertain_phrases) | |
| def generate_text(prompt): | |
| """Generate a response from the text generation model.""" | |
| messages = [{"role": "user", "content": prompt}] | |
| response = "" | |
| for message in client.chat_completion(messages, max_tokens=512, stream=True, temperature=0.7, top_p=0.95): | |
| token = message.choices[0].delta.content | |
| response += token | |
| if is_uncertain(prompt, response): | |
| return "AI is uncertain about the response." | |
| return response | |
| # Function to detect language and generate speech | |
| def text_to_speech(text): | |
| lang = detect(text) # Detect language | |
| wav_path = "./output.wav" | |
| if lang == "sw": # Swahili | |
| swahili_tts.synthesis(text, wav_path=wav_path) | |
| else: # Default to English if not Swahili | |
| english_tts.synthesis(text, wav_path=wav_path) | |
| return wav_path | |
| def process_audio(audio): | |
| # Step 1: Transcribe the audio | |
| transcription = transcribe_auto(audio) | |
| # Step 2: Generate text based on the transcription | |
| generated_text = generate_text(transcription) | |
| # Step 3: Convert the generated text to speech | |
| speech = text_to_speech(generated_text) | |
| return transcription, generated_text, speech | |
| # Gradio Interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("<p align='center' style='font-size: 20px;'>End-to-End ASR, Text Generation, and TTS</p>") | |
| gr.HTML("<center>Upload or record audio. The model will transcribe, generate a response, and read it out.</center>") | |
| audio_input = gr.Audio(label="Input Audio", type="filepath") | |
| text_output = gr.Textbox(label="Transcription") | |
| generated_text_output = gr.Textbox(label="Generated Text") | |
| audio_output = gr.Audio(label="Output Speech") | |
| submit_btn = gr.Button("Submit") | |
| submit_btn.click( | |
| fn=process_audio, | |
| inputs=audio_input, | |
| outputs=[text_output, generated_text_output, audio_output] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |