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| import concurrent | |
| import os | |
| import tempfile | |
| from typing import Optional, Tuple | |
| import numpy as np | |
| import spaces | |
| from transformers import pipeline | |
| import gradio as gr | |
| import torch | |
| import torchaudio | |
| from resemble_enhance.enhancer.inference import denoise, enhance | |
| from flore200_codes import flores_codes | |
| from tts import BambaraTTS | |
| # Check if CUDA is available | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # Translation pipeline | |
| translation_model = "oza75/nllb-600M-mt-french-bambara" | |
| # translation_model = "oza75/nllb-1.3B-mt-french-bambara" | |
| translator = pipeline("translation", model=translation_model, max_length=512) | |
| # Text-to-Speech pipeline | |
| tts_model = "oza75/bambara-tts" | |
| tts = BambaraTTS(tts_model) | |
| # Function to translate text to Bambara | |
| def translate_to_bambara(text, src_lang): | |
| translation = translator(text, src_lang=src_lang, tgt_lang="bam_Latn") | |
| return str(translation[0]['translation_text']) | |
| # Function to convert text to speech | |
| def text_to_speech(bambara_text, reference_speaker: str, reference_audio: Optional[Tuple] = None): | |
| if reference_audio is not None: | |
| ref_sr, ref_audio = reference_audio | |
| ref_audio = torch.from_numpy(ref_audio) | |
| # Add a channel dimension if the audio is 1D | |
| if ref_audio.ndim == 1: | |
| ref_audio = ref_audio.unsqueeze(0) | |
| # Save the reference audio to a temporary file if it's not None | |
| with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp: | |
| torchaudio.save(tmp.name, ref_audio, ref_sr) | |
| tmp_path = tmp.name | |
| # Use the temporary file as the speaker reference | |
| sr, audio = tts.text_to_speech(bambara_text, speaker_reference_wav_path=tmp_path, enable_text_splitting=True) | |
| # Clean up the temporary file | |
| os.unlink(tmp_path) | |
| else: | |
| # If no reference audio provided, proceed with the reference_speaker | |
| sr, audio = tts.text_to_speech(bambara_text, speaker_reference_wav_path=reference_speaker) | |
| audio = audio.mean(dim=0) | |
| return audio, sr | |
| # Function to enhance speech | |
| def enhance_speech(audio_array, sampling_rate, solver, nfe, tau, denoise_before_enhancement): | |
| solver = solver.lower() | |
| nfe = int(nfe) | |
| lambd = 0.9 if denoise_before_enhancement else 0.1 | |
| def denoise_audio(): | |
| try: | |
| return denoise(audio_array, sampling_rate, device) | |
| except Exception as e: | |
| print("> Error while denoising : ", str(e)) | |
| return audio_array, sampling_rate | |
| def enhance_audio(): | |
| try: | |
| return enhance(audio_array, sampling_rate, device, nfe=nfe, solver=solver, lambd=lambd, tau=tau) | |
| except Exception as e: | |
| print("> Error while enhancement : ", str(e)) | |
| return audio_array, sampling_rate | |
| with concurrent.futures.ThreadPoolExecutor() as executor: | |
| future_denoise = executor.submit(denoise_audio) | |
| future_enhance = executor.submit(enhance_audio) | |
| denoised_audio, new_sr1 = future_denoise.result() | |
| enhanced_audio, new_sr2 = future_enhance.result() | |
| # Convert to numpy and return | |
| return (new_sr1, denoised_audio.cpu().numpy()), (new_sr2, enhanced_audio.cpu().numpy()) | |
| # Define the Gradio interface | |
| def _fn( | |
| src_lang, | |
| text, | |
| reference_speaker, | |
| reference_audio=None, | |
| solver="Midpoint", | |
| nfe=128, | |
| prior_temp=0.01, | |
| denoise_before_enhancement=False | |
| ): | |
| source_lang = flores_codes[src_lang] | |
| reference_speaker = os.path.join("./audios", reference_speaker) | |
| # Step 1: Translate the text to Bambara | |
| bambara_text = translate_to_bambara(text, source_lang) | |
| yield bambara_text, None, None, None | |
| # Step 2: Convert the translated text to speech with reference audio | |
| if reference_audio is not None: | |
| audio_array, sampling_rate = text_to_speech(bambara_text, reference_speaker, reference_audio) | |
| else: | |
| audio_array, sampling_rate = text_to_speech(bambara_text, reference_speaker=reference_speaker) | |
| yield bambara_text, (sampling_rate, audio_array.numpy()), None, None | |
| # Step 3: Enhance the audio | |
| denoised_audio, enhanced_audio = enhance_speech( | |
| audio_array, | |
| sampling_rate, | |
| solver, | |
| nfe, | |
| prior_temp, | |
| denoise_before_enhancement | |
| ) | |
| yield bambara_text, (sampling_rate, audio_array.numpy()), denoised_audio, enhanced_audio | |
| def main(): | |
| lang_codes = list(flores_codes.keys()) | |
| # List all files in the ./audios directory for the dropdown | |
| audio_files = [f for f in os.listdir('./audios') if os.path.isfile(os.path.join('./audios', f))] | |
| # Build Gradio app | |
| app = gr.Interface( | |
| fn=_fn, | |
| inputs=[ | |
| gr.Dropdown(label="Source Language", choices=lang_codes, value='French'), | |
| gr.Textbox(label="Text to Translate", lines=3, value="Thomas Isidore Noël Sankara est fils d'un père Peul — originaire du village de Sitoèga dans le département de Bokin dans la province du Passoré — et d'une mère mossi, et grandit entre valeurs militaires et religiosité chrétienne."), | |
| gr.Dropdown(label="Voice", choices=audio_files, value="male_3.wav"), | |
| gr.Audio(label="Clone your voice (optional)", type="numpy", format="wav", elem_id="clone_voice_input"), | |
| # gr.Dropdown( | |
| # choices=["Midpoint", "RK4", "Euler"], value="Midpoint", | |
| # label="ODE Solver (Midpoint is recommended)" | |
| # ), | |
| # gr.Slider(minimum=1, maximum=128, value=64, step=1, label="Number of Function Evaluations"), | |
| # gr.Slider(minimum=0.1, maximum=1, value=0.5, step=0.01, label="Prior Temperature"), | |
| # gr.Checkbox(value=False, label="Denoise Before Enhancement") | |
| ], | |
| outputs=[ | |
| gr.Textbox(label="Translated Text"), | |
| gr.Audio(label="Original TTS Audio", format='wav'), | |
| gr.Audio(label="Denoised Audio", format='wav'), | |
| gr.Audio(label="Enhanced Audio", format='wav') | |
| ], | |
| examples=[ | |
| ['French', "Mais il n'avait pas l'air content. Je lui ai même dit : « Ce n'est pas de ma faute. » Il n'a pas répondu. J'ai pensé alors que je n'aurais pas dû lui dire cela. En somme, je n'avais pas à m'excuser. C'était plutôt à lui de me présenter ses condoléances.", "male_3.wav", None], | |
| ['French', "l'asile de vieillards est à Marengo, à quatre-vingts kilomètres d'Alger. Je prendrai l'autobus à deux heures et j'arriverai dans l'après-midi. Ainsi, je pourrai veiller et je rentrerai demain soir. J'ai demandé deux jours de congé à mon patron et il ne pouvait pas me les refuser avec une excuse pareille", "male_3.wav", None], | |
| ['English', "Today, my mother is dead. Or maybe yesterday, I don't know. I received a telegram from the asylum: “Mother deceased. Hand burial. Distinguished feelings.“ It does not mean anything. Maybe it was yesterday.", "male_2.wav", None], | |
| ], | |
| css="#clone_voice_input .audio-container button.boundedheight { height: 147px !important; }", | |
| title="Bambara Translation and Text to Speech with Audio Enhancement", | |
| description="Translate text to Bambara and convert it to speech with options to enhance audio quality." | |
| ) | |
| app.launch(share=False) | |
| if __name__ == "__main__": | |
| main() | |