Create app.py

app.py

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+import os
+import tempfile
+import torch
+import numpy as np
+import gradio as gr
+import scipy.io.wavfile as wavfile
+from pydub import AudioSegment
+from transformers import VitsModel, AutoTokenizer
+
+# ---------- Configuration --------------------------------------------------
+# Define available TTS models here. Add new entries as needed.
+TTS_MODELS = {
+    "Swahili": {
+        "tokenizer": "FarmerlineML/swahili-tts-2025",
+        "checkpoint": "FarmerlineML/Swahili-tts-2025_part4"
+    },
+    "Krio": {
+        "tokenizer": "facebook/mms-tts-kri",
+        "checkpoint": "facebook/mms-tts-kri"
+    },
+}
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# ---------- Load all models & tokenizers -----------------------------------
+models = {}
+tokenizers = {}
+for name, paths in TTS_MODELS.items():
+    print(f"Loading {name} model...")
+    model = VitsModel.from_pretrained(paths["checkpoint"]).to(device)
+    model.eval()
+    # Apply clear-speech inference parameters (tweak per model if desired)
+    model.noise_scale = 0.7
+    model.noise_scale_duration = 0.667
+    model.speaking_rate = 0.75
+    models[name] = model
+    tokenizers[name] = AutoTokenizer.from_pretrained(paths["tokenizer"])
+
+# ---------- Utility: WAV ➔ MP3 Conversion -----------------------------------
+def _wav_to_mp3(wave_np: np.ndarray, sr: int) -> str:
+    """Convert int16 numpy waveform to an MP3 temp file, return its path."""
+    # Ensure int16 for pydub
+    if wave_np.dtype != np.int16:
+        wave_np = (wave_np * 32767).astype(np.int16)
+
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tf:
+        wavfile.write(tf.name, sr, wave_np)
+        wav_path = tf.name
+
+    mp3_path = wav_path.replace(".wav", ".mp3")
+    AudioSegment.from_wav(wav_path).export(mp3_path, format="mp3", bitrate="64k")
+    os.remove(wav_path)
+    return mp3_path
+
+# ---------- TTS Generation ---------------------------------------------------
+def tts_generate(model_name: str, text: str):
+    """Generate speech for `text` using the selected model."""
+    if not text:
+        return None
+    model = models[model_name]
+    tokenizer = tokenizers[model_name]
+    inputs = tokenizer(text, return_tensors="pt").to(device)
+    with torch.no_grad():
+        wave = model(**inputs).waveform[0].cpu().numpy()
+    return _wav_to_mp3(wave, model.config.sampling_rate)
+
+# ---------- Gradio Interface ------------------------------------------------
+examples = [
+    ["Swahili", "zao kusaidia kuondoa umaskini na kujenga kampeni za mwamko wa virusi vya ukimwi amezitembelea"],
+    ["Swahili", "Kidole hiki ni tofauti na vidole vingine kwa sababu mwelekeo wake ni wa pekee."],
+    ["Swahili", "Tafadhali hakikisha umefunga mlango kabla ya kuondoka."],
+    ["Krio", "Wetin na yu nem?"],
+    ["Krio", "Usai yu kɔmɔt?"],
+    ["Krio", "A gladi fɔ mit yu."],
+]
+
+demo = gr.Interface(
+    fn=tts_generate,
+    inputs=[
+        gr.Dropdown(choices=list(TTS_MODELS.keys()), default="Swahili", label="Choose TTS Model"),
+        gr.Textbox(lines=3, placeholder="Enter text here", label="Input Text")
+    ],
+    outputs=gr.Audio(type="filepath", label="Audio", autoplay=True),
+    title="Multi‐Model Text-to-Speech",
+    description=(
+        "Select a TTS model from the dropdown and enter text to generate speech."
+    ),
+    examples=examples,
+    cache_examples=True,
+)
+
+if __name__ == "__main__":
+    demo.launch()