| import gradio as gr |
| from transformers import VitsModel, AutoTokenizer |
| import torch |
| import logging |
| import spaces |
| from typing import Tuple, Optional |
| import numpy as np |
|
|
| logging.basicConfig(level=logging.INFO) |
| logger = logging.getLogger(__name__) |
|
|
| if torch.cuda.is_available(): |
| device = "cuda" |
| logger.info("Using CUDA for inference.") |
| elif torch.backends.mps.is_available(): |
| device = "mps" |
| logger.info("Using MPS for inference.") |
| else: |
| device = "cpu" |
| logger.info("Using CPU for inference.") |
|
|
| languages = ["bambara", "boomu", "dogon", "pular", "songhoy", "tamasheq"] |
|
|
| examples = { |
|
|
| "bambara": "An filɛ ni ye yɔrɔ minna ni an ye an sigi ka a layɛ yala an bɛ ka baara min kɛ ɛsike a kɛlen don ka Ɲɛ wa ?", |
| |
| "boomu": "Vunurobe wozomɛ pɛɛ, Poli we zo woro han Deeɓenu wara li Deeɓenu faralo zuun. Lo we baba a lo wara yi see ɓa Zuwifera ma ɓa Gɛrɛkela wa.", |
| |
| "dogon": "Pɔɔlɔ, kubɔ lugo joo le, bana dɛin dɛin le, inɛw Ama titiyaanw le digɛu, Ama, emɛ babe bɛrɛ sɔɔ sɔi.", |
| |
| "pular": "Miɗo ndaarde saabe Laamɗo e saabe Iisaa Almasiihu caroyoowo wuurɓe e maayɓe oo, miɗo ndaardire saabe gartol makko ka num e Laamu makko", |
| |
| "songhoy": "Haya ka se beenediyo kokoyteraydi go hima nda huukoy foo ka fatta ja subaahi ka taasi goykoyyo ngu rezẽ faridi se", |
| |
| "tamasheq": "Toḍă tăfukt ɣas, issăɣră-dd măssi-s n-ašĕkrĕš ănaẓraf-net, inn'-as: 'Ǝɣĕr-dd inaxdimăn, tĕẓlĕd-asăn, sănt s-wi dd-ĕšrăynen har tĕkkĕd wi dd-ăzzarnen." |
|
|
| } |
|
|
| class MalianTTS: |
| def __init__(self, model_name: str = "MALIBA-AI/malian-tts"): |
| self.model_name = model_name |
| self.models = {} |
| self.tokenizers = {} |
| self._load_models() |
| |
| def _load_models(self): |
| """Load all language models and tokenizers""" |
| try: |
| for lang in languages: |
| logger.info(f"Loading model and tokenizer for {lang}...") |
| self.models[lang] = VitsModel.from_pretrained( |
| self.model_name, |
| subfolder=f"models/{lang}" |
| ).to(device) |
| self.tokenizers[lang] = AutoTokenizer.from_pretrained( |
| self.model_name, |
| subfolder=f"models/{lang}" |
| ) |
| logger.info(f"Successfully loaded {lang}") |
| except Exception as e: |
| logger.error(f"Failed to load models: {str(e)}") |
| raise Exception(f"Model loading failed: {str(e)}") |
| |
| def synthesize(self, language: str, text: str) -> Tuple[Optional[Tuple[int, np.ndarray]], Optional[str]]: |
| if not text.strip(): |
| return None, "Please enter some text to synthesize." |
| |
| try: |
| model = self.models[language] |
| tokenizer = self.tokenizers[language] |
| |
| inputs = tokenizer(text, return_tensors="pt").to(device) |
| |
| with torch.no_grad(): |
| output = model(**inputs).waveform |
| |
| waveform = output.squeeze().cpu().numpy() |
| sample_rate = model.config.sampling_rate |
| |
| return (sample_rate, waveform), None |
| |
| except Exception as e: |
| logger.error(f"Error during inference for {language}: {str(e)}") |
| return None, f"Error generating audio: {str(e)}" |
|
|
| |
| tts_system = MalianTTS() |
|
|
| @spaces.GPU() |
| def generate_audio(language: str, text: str) -> Tuple[Optional[Tuple[int, np.ndarray]], str]: |
| """ |
| Generate audio from text using the specified language model. |
| """ |
| if not text.strip(): |
| return None, "Please enter some text to synthesize." |
| |
| try: |
| audio_output, error_msg = tts_system.synthesize(language, text) |
| if error_msg: |
| logger.error(f"TTS generation failed: {error_msg}") |
| return None, error_msg |
| |
| logger.info(f"Successfully generated audio for {language}") |
| return audio_output, "Audio generated successfully!" |
| |
| except Exception as e: |
| logger.error(f"Audio generation failed: {e}") |
| return None, f"Error: {str(e)}" |
|
|
| def load_example(language: str) -> str: |
| """Load example text for the selected language""" |
| return examples.get(language, "No example available") |
|
|
| def build_interface(): |
| """ |
| Builds the Gradio interface for Malian TTS. |
| """ |
| with gr.Blocks(title="MalianVoices") as demo: |
| gr.Markdown( |
| """ |
| # MalianVoices: 🇲🇱 Text-to-Speech in Six Malian Languages |
| |
| Lightweight TTS for six Malian languages: **Bambara, Boomu, Dogon, Pular, Songhoy, Tamasheq**. |
| |
| - ✅ Real-time TTS with fast response |
| |
| ## How to Use |
| 1. Pick a language from the dropdown |
| 2. Enter your text or load an example |
| 3. Click **"Generate Audio"** to listen |
| """ |
| ) |
| |
| with gr.Row(): |
| language = gr.Dropdown( |
| choices=languages, |
| label="Language", |
| value="bambara" |
| ) |
| |
| with gr.Column(): |
| text = gr.Textbox( |
| label="Input Text", |
| lines=5, |
| placeholder="Type your text here..." |
| ) |
| |
| with gr.Row(): |
| example_btn = gr.Button("Load Example") |
| generate_btn = gr.Button("Generate Audio", variant="primary") |
| |
| audio_output = gr.Audio(label="Generated Audio", type="numpy") |
| status_msg = gr.Textbox(label="Status", interactive=False) |
| |
| |
| gr.Markdown( |
| """ |
| By [sudoping01](https://huggingface.co/sudoping01), from [sudoping01/malian-tts](https://huggingface.co/sudoping01/malian-tts). |
| Fine-tuned on Meta's MMS, CC BY-NC 4.0, non-commercial. |
| """ |
| ) |
| |
| |
| generate_btn.click( |
| fn=generate_audio, |
| inputs=[language, text], |
| outputs=[audio_output, status_msg] |
| ) |
| |
| example_btn.click( |
| fn=load_example, |
| inputs=language, |
| outputs=text |
| ) |
| |
| return demo |
|
|
| if __name__ == "__main__": |
| logger.info("Starting the Gradio interface for MalianVoices TTS.") |
| interface = build_interface() |
| interface.launch() |
| logger.info("Gradio interface running.") |
