Create app.py

app.py

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+import gradio as gr
+from transformers import VitsModel, AutoTokenizer
+import torch
+import logging
+
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+languages = ["bambara", "boomu", "dogon", "pular", "songhoy", "tamasheq"]
+
+
+models = {}
+tokenizers = {}
+
+
+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": "Issăɣlăy-tăn Ɣisa tangalt ḍarăt-a-wen, inn'-asăn: "Tĕmmĕnĕya n-Măssinăɣ, tănifăqqa d-ăhalĕs ilan ašĕkrĕš-net; ăffăw-t ɣas, ifăl ehăn-net, immăɣ i-inaxdimăn issăxdăm ašĕkrĕš-net n-lăɣnăb"
+}
+
+
+try:
+    for lang in languages:
+        logger.info(f"Loading model and tokenizer for {lang}...")
+        models[lang] = VitsModel.from_pretrained("sudoping01/malian-tts", subfolder=f"models/{lang}")
+        tokenizers[lang] = AutoTokenizer.from_pretrained("sudoping01/malian-tts", 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 generate_audio(language, text):
+    if not text.strip():
+        return None, "Please enter some text to synthesize."
+    
+    try:
+  
+        model = models[language]
+        tokenizer = tokenizers[language]
+        
+
+        inputs = tokenizer(text, return_tensors="pt")
+        
+
+        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)}"
+
+def load_example(language):
+    return examples.get(language, "No example available")
+
+
+with gr.Blocks(title="Malian Languages TTS") as demo:
+    gr.Markdown("# Malian Languages Text-to-Speech")
+    gr.Markdown("Select a language, enter text or load an example, and listen to the synthesized speech!")
+    
+    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...")
+            example_btn = gr.Button("Load Example")
+    
+    generate_btn = gr.Button("Generate Audio", variant="primary")
+    audio_output = gr.Audio(label="Generated Audio", type="numpy")
+    error_msg = gr.Textbox(label="Status", visible=False)
+    
+    # Connect buttons to functions
+    generate_btn.click(
+        fn=generate_audio,
+        inputs=[language, text],
+        outputs=[audio_output, error_msg]
+    )
+    example_btn.click(
+        fn=load_example,
+        inputs=language,
+        outputs=text
+    )
+
+
+demo.launch()