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| import os | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from groq import Groq | |
| from transformers import pipeline | |
| from TTS.api import TTS | |
| MODEL_NAME = "openai/whisper-large-v3" | |
| BATCH_SIZE = 8 | |
| FILE_LIMIT_MB = 1000 | |
| YT_LENGTH_LIMIT_S = 3600 # limit to 1 hour YouTube files | |
| device = 0 if torch.cuda.is_available() else "cpu" | |
| pipe = pipeline( | |
| task="automatic-speech-recognition", | |
| model=MODEL_NAME, | |
| chunk_length_s=30, | |
| device=device, | |
| ) | |
| def use_pipe(inputs): | |
| if inputs is None: | |
| raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.") | |
| text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": "transcribe"}, return_timestamps=True)["text"] | |
| return text | |
| groq_client = Groq(api_key=os.getenv('GROQ_API_KEY')) | |
| def transcribe(stream, new_chunk): | |
| """ | |
| Transcribes using whisper | |
| """ | |
| sr, y = new_chunk | |
| # Convert stereo to mono if necessary | |
| if y.ndim == 2 and y.shape[1] == 2: | |
| y = y.mean(axis=1) # Averaging both channels if stereo | |
| y = y.astype(np.float32) | |
| # Normalization | |
| y /= np.max(np.abs(y)) | |
| if stream is not None: | |
| stream = np.concatenate([stream, y]) | |
| else: | |
| stream = y | |
| return stream, use_pipe(stream) | |
| def autocomplete(text): | |
| """ | |
| Autocomplete the text using Gemma. | |
| """ | |
| if text != "": | |
| response = groq_client.chat.completions.create( | |
| model='llama3-8b-8192', | |
| messages=[{"role": "system", "content": "Tu es une assistante tres polis, tu ne repond que en francais et uniquement en utilisant le vous et jamais le tu"}, | |
| {"role": "user", "content": text}] | |
| ) | |
| return response.choices[0].message.content | |
| def process_audio(input_audio, new_chunk): | |
| """ | |
| Process the audio input by transcribing and completing the sentences. | |
| Accumulate results to return to Gradio interface. | |
| """ | |
| stream, transcription = transcribe(input_audio, new_chunk) | |
| text = autocomplete(transcription) | |
| print (transcription, text) | |
| api = TTS("tts_models/multilingual/multi-dataset/xtts", gpu=True) | |
| api.tts_to_file(text, file_path="output.wav", language="fr") | |
| audio = "./output.wav" | |
| return stream, text, audio | |
| demo = gr.Interface( | |
| fn = process_audio, | |
| inputs = ["state", gr.Audio(sources=["microphone"], streaming=True)], | |
| outputs = ["state", gr.Markdown(), gr.Audio(interactive=False, autoplay=True)], | |
| title="Parlons nous ☎️", | |
| description="Powered by [whisper-base-en](https://huggingface.co/openai/whisper-base.en), and [gemma-7b-it](https://huggingface.co/google/gemma-7b-it) (via [Groq](https://groq.com/))", | |
| live=True, | |
| allow_flagging="never" | |
| ) | |
| demo.launch() | |