app.py

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 = {
    "Ewe": {
        "tokenizer": "FarmerlineML/Ewe-tts-2025_v2",
        "checkpoint": "FarmerlineML/Ewe-tts-2025_v2"
    },
    "Swahili": {
        "tokenizer": "FarmerlineML/swahili-tts-2025",
        "checkpoint": "FarmerlineML/Swahili-tts-2025_part4"
    },
    "Krio": {
        "tokenizer": "FarmerlineML/Krio-TTS",
        "checkpoint": "FarmerlineML/Krio-TTS"
    },
    
}

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 = 0.8
    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 = [
    ["Ewe", "kpovitɔwo dometɔ ɖeka lé kaƒomɔ ɖe asi eye wòɖo ŋɔtsitsyɔnu."],
    ["Ewe", "ɖeviawo ƒe gbɔsɔsɔ me anɔ abe enyi. fi si ɖeviwo le la ƒo ɖi. ɖeviawo kɔ nu kake aɖewo ɖe asi ɖewo hā nɔ wonuiwo kplɔm."],
    ["Ewe", "amewo le yɔƒe me eye aɖake le wogbɔ. wodo awu yibɔ ŋutsu aɖe le kponyi fam le akɔ fam ne nyɔnu aɖe."],
    ["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", "aw yu de du"],
    ["Krio", "A de go skul"],
]

demo = gr.Interface(
    fn=tts_generate,
    inputs=[
        gr.Dropdown(choices=list(TTS_MODELS.keys()), value="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()