app.py

# app.py — TalkClone (HF Space, 1-column, persistent output, DownloadButton)

import os, re, tempfile, shutil, time
import numpy as np
import soundfile as sf
import gradio as gr

os.environ.setdefault("COQUI_TOS_AGREED", "1")

MODEL_NAME = "tts_models/multilingual/multi-dataset/xtts_v2"

LANGS = [
    ("English", "en"),
    ("Spanish", "es"),
    ("French",  "fr"),
    ("German",  "de"),
    ("Italian", "it"),
    ("Portuguese", "pt"),
    ("Polish",  "pl"),
    ("Turkish", "tr"),
    ("Russian", "ru"),
    ("Dutch",   "nl"),
    ("Czech",   "cs"),
    ("Arabic",  "ar"),
    ("Chinese (Simplified)", "zh-cn"),
    ("Hungarian", "hu"),
    ("Korean",  "ko"),
    ("Japanese","ja"),
    ("Hindi",   "hi"),
]
LANG_LABELS = [name for name, _ in LANGS]
LANG_MAP = {name: code for name, code in LANGS}

_tts = None
def get_tts():
    global _tts
    if _tts is not None:
        return _tts
    try:
        import torch
        try:
            torch.set_num_threads(max(1, min(4, os.cpu_count() or 2)))
        except Exception:
            pass
        use_gpu = torch.cuda.is_available()
    except Exception:
        use_gpu = False
    from TTS.api import TTS
    try:
        _tts = TTS(MODEL_NAME, gpu=use_gpu)
    except TypeError:
        _tts = TTS(MODEL_NAME)
    return _tts

def clean_text(t: str) -> str:
    return " ".join((t or "").strip().split())

def synth_to_file_safe(tts, txt, out_path, wav_path, lang, speed):
    try:
        tts.tts_to_file(text=txt, file_path=out_path,
                        speaker_wav=wav_path, language=lang, speed=speed)
    except TypeError:
        tts.tts_to_file(text=txt, file_path=out_path,
                        speaker_wav=wav_path, language=lang)

def safe_filename(seed_text: str, lang_code: str) -> str:
    base = clean_text(seed_text)[:40] or "talkclone"
    base = re.sub(r"[^A-Za-z0-9_-]+", "_", base).strip("_")
    ts = time.strftime("%Y%m%d-%H%M%S")
    return f"{base}_{lang_code}_{ts}.wav"

def tts_clone(text, ref_audio, lang_label, speed, split_sentences, progress=gr.Progress(track_tqdm=True)):
    if ref_audio is None:
        raise gr.Error("Upload a reference voice (10–60s, clean speech).")
    text = clean_text(text)
    if not text:
        raise gr.Error("Please enter some text.")
    if len(text) > 1400 and not split_sentences:
        raise gr.Error("Text is very long. Enable 'Auto split' or paste a shorter chunk on CPU.")

    lang = LANG_MAP.get(lang_label, "en")
    wav_path = ref_audio

    chunks = [text]
    if split_sentences:
        rough = [s.strip() for s in re.split(r'(?<=[.!?؟۔]|[\u0964\u0965])\s+', text) if s.strip()]
        chunks = []
        for s in rough:
            if len(s) <= 220:
                chunks.append(s)
            else:
                for i in range(0, len(s), 200):
                    chunks.append(s[i:i+200])

    tts = get_tts()
    out_wavs = []
    with tempfile.TemporaryDirectory() as td:
        total = max(len(chunks), 1)
        for i, chunk in enumerate(chunks, 1):
            progress((i-1)/total, desc=f"Synthesizing {i}/{total}")
            part_path = os.path.join(td, f"part_{i}.wav")
            synth_to_file_safe(tts, chunk, part_path, wav_path, lang, speed)
            data, sr = sf.read(part_path)
            out_wavs.append((data, sr))

    # concat
    if len(out_wavs) == 1:
        final_data, sr = out_wavs[0]
    else:
        sr = out_wavs[0][1]
        final_data = np.concatenate([d for d, _ in out_wavs], axis=0)

    # write to persistent temp + copy to a nice-named path for downloading
    ntf = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
    ntf_path = ntf.name
    ntf.close()
    sf.write(ntf_path, final_data, sr)

    pretty_name = os.path.join("/tmp", safe_filename(text, lang))
    try:
        shutil.copyfile(ntf_path, pretty_name)
        dl_path = pretty_name
    except Exception:
        dl_path = ntf_path  # fallback

    # return both: audio preview path, and a file path for DownloadButton
    return ntf_path, dl_path

CUSTOM_CSS = """
.gradio-container { max-width: 860px !important; margin: 0 auto; }
#wrap, #ref, #lang, #txt, #spd, #split, #out_audio, #dl {
  background: #f8fafc !important;
  border: 1px solid #e5e7eb !important;
  border-radius: 14px !important;
  padding: 14px !important;
}
#ref, #out_audio, #dl { background: #eef2ff !important; }
#gen button, #gen { background: #10b981 !important; color: #fff !important; }
#gen button:hover { filter: brightness(0.95); }
/* hide HF/Gradio chrome */
footer, .footer, #footer,
a[href*="gradio.live"], a[href*="gradio.app"], a[href*="/api"], a[href*="hf.space"],
button[aria-label="Settings"],
[data-testid="block-analytics"], [data-testid="embed-info"] { display: none !important; }
"""

with gr.Blocks(title="TalkClone - Voice Cloning & TTS", css=CUSTOM_CSS, analytics_enabled=False) as demo:
    with gr.Column(elem_id="wrap"):
        gr.Markdown("## TalkClone — Text-to-Speech with Voice Cloning")
        gr.Markdown("Upload a short **reference voice** (10–60s), choose **language**, enter **text**, then **Generate**. "
                    "On free CPU, keep text short or enable **Auto split** for speed.")

        ref_audio = gr.Audio(label="Reference Voice (WAV/MP3)", type="filepath", elem_id="ref")
        language  = gr.Dropdown(choices=LANG_LABELS, value="English", label="Language", elem_id="lang")
        text      = gr.Textbox(label="Text", lines=6, placeholder="Type or paste your text here…", elem_id="txt")
        speed     = gr.Slider(0.7, 1.3, value=1.0, step=0.05, label="Speed", elem_id="spd")
        split     = gr.Checkbox(value=True, label="Auto split long text by sentence", elem_id="split")
        submit    = gr.Button("Generate", variant="primary", elem_id="gen")

        output   = gr.Audio(label="Cloned Speech", type="filepath", interactive=False, elem_id="out_audio")
        download = gr.DownloadButton(label="Download audio", elem_id="dl")

        def run_and_return(text, ref_audio, language, speed, split):
            audio_path, dl_path = tts_clone(text, ref_audio, language, speed, split)
            # set button to download the file we just wrote
            return audio_path, gr.update(value=dl_path, label=f"Download ({os.path.basename(dl_path)})")

        submit.click(run_and_return,
                     inputs=[text, ref_audio, language, speed, split],
                     outputs=[output, download])

if __name__ == "__main__":
    port = int(os.environ.get("PORT", "7860"))
    try:
        demo.queue().launch(server_name="0.0.0.0", server_port=port, show_error=True, show_api=False)
    except TypeError:
        demo.launch(server_name="0.0.0.0", server_port=port, show_error=True, show_api=False)