Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| from transformers import pipeline, AutoModelForSpeechSeq2Seq, AutoProcessor | |
| import torch | |
| import numpy as np | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| #device = "cpu" | |
| torch_dtype = torch.float16 if device != "cpu" else torch.float32 | |
| print("Device:", device) | |
| model_id = "openai/whisper-large-v3" | |
| #model_id = "openai/whisper-medium" | |
| # model_id = "openai/whisper-large-v3" | |
| # model_id = "openai/whisper-medium" | |
| # model = AutoModelForSpeechSeq2Seq.from_pretrained( | |
| # model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True | |
| # ) | |
| # model.to(device) | |
| # processor = AutoProcessor.from_pretrained(model_id) | |
| # pipe_transcription = pipeline( | |
| # "automatic-speech-recognition", | |
| # model=model, | |
| # tokenizer=processor.tokenizer, | |
| # feature_extractor=processor.feature_extractor, | |
| # max_new_tokens=128, | |
| # chunk_length_s=30, | |
| # batch_size=16, | |
| # return_timestamps=True, | |
| # torch_dtype=torch_dtype, | |
| # device=device, | |
| # ) | |
| pipe_transcription = pipeline("automatic-speech-recognition", model="pierreguillou/whisper-medium-french") | |
| pipe_translate = pipeline("translation", model="Helsinki-NLP/opus-mt-fr-en", device=device) | |
| pipe_tts = pipeline("text-to-speech", model="facebook/mms-tts-eng", device=device) # Better quality, way faster than bark | |
| def get_translation(text): | |
| return pipe_translate(text)[0]["translation_text"] | |
| def get_transcript(voice): | |
| return get_translation(pipe_transcription(voice)["text"])#, generate_kwargs={"task": "translate", "language": "french"})["text"] | |
| def get_audio(text): | |
| speech = pipe_tts(text) | |
| return speech["sampling_rate"], (speech["audio"]* 32767).astype(np.int16).T | |
| with gr.Blocks() as demo: | |
| with gr.Tab("Texte (rapide)"): | |
| input_text = gr.Textbox( | |
| label="Input text", | |
| info="Your text", | |
| lines=3, | |
| placeholder="Écrire le texte à traduire", | |
| ) | |
| translation_button = gr.Button("Traduire...") | |
| output_text = gr.Textbox( | |
| label="Output text", | |
| info="Your text", | |
| lines=3, | |
| placeholder="Votre traduction", | |
| ) | |
| speech_button = gr.Button("Générer audio...") | |
| translation_button.click( | |
| get_translation, | |
| inputs=[ | |
| input_text | |
| ], | |
| outputs=[ | |
| output_text | |
| ], | |
| ) | |
| speech_button.click( | |
| get_audio, | |
| inputs=[ | |
| output_text | |
| ], | |
| outputs=[ | |
| gr.Audio(label="Output") | |
| ], | |
| ) | |
| with gr.Tab("Voix (plus lent)"): | |
| voice = gr.Audio(sources=["microphone"], type="filepath") | |
| translation_button = gr.Button("Traduire votre enregistrement !") | |
| output_text = gr.Textbox( | |
| label="Texte traduit", | |
| info="Votre texte", | |
| lines=3, | |
| placeholder="Votre traduction", | |
| ) | |
| speech_button = gr.Button("Générer audio !") | |
| translation_button.click( | |
| get_transcript, | |
| inputs=[ | |
| voice | |
| ], | |
| outputs=[ | |
| output_text | |
| ], | |
| ) | |
| speech_button.click( | |
| get_audio, | |
| inputs=[ | |
| output_text | |
| ], | |
| outputs=[ | |
| gr.Audio(label="Output") | |
| ], | |
| ) | |
| demo.launch() |