| """Hugging Face Space (ZeroGPU): drop 20-30 s of someone talking, type Nepali, English or both, hear it in their voice. |
| Same pipeline as app/cloner.py (pipeline/autoclone.py), made to fit a ZeroGPU Space: one process, Kriti from ONNX |
| (KRITI_ONNX, no NeMo), weights pulled from a private model repo (MODEL_REPO, read with the HF_TOKEN secret). |
| GPU work runs inside @spaces.GPU functions; outside a Space the decorator is a no-op. |
| Preset voices: MODEL_REPO/presets/<name>/ (voice.json + pieces, built by ops/build_presets.py from data/voices.json). |
| English is spoken from Nepali-letter English (frontend/english.py), the way the user preferred by ear; BILINGUAL=0 so |
| the speaker's own English prompts are not used for English lines. |
| Files: deploy with ops/deploy_space_clone.sh (copies pipeline/, frontend/, tools/ and data/ next to this file).""" |
| import hashlib |
| import json |
| import os |
| import sys |
|
|
| import gradio as gr |
|
|
| HERE = os.path.dirname(os.path.abspath(__file__)) |
| sys.path[:0] = [os.path.join(HERE, "pipeline"), os.path.join(HERE, "tools"), HERE] |
|
|
| try: |
| import spaces |
| gpu = spaces.GPU |
| except ImportError: |
| def gpu(*a, **k): |
| return a[0] if a and callable(a[0]) else (lambda f: f) |
|
|
| if os.environ.get("MODEL_REPO"): |
| from huggingface_hub import snapshot_download |
| d = snapshot_download(os.environ["MODEL_REPO"], token=os.environ.get("HF_TOKEN")) |
| os.environ.setdefault("F5_CKPT", os.path.join(d, os.environ.get("MODEL_FILE", "podvoices_ema.pt"))) |
| |
| if os.path.exists(os.path.join(d, os.environ.get("EN_MODEL_FILE", "swar_v6_ema.pt"))): |
| os.environ.setdefault("F5_EN_CKPT", os.path.join(d, os.environ.get("EN_MODEL_FILE", "swar_v6_ema.pt"))) |
| os.environ.setdefault("PRESETS_DIR", os.path.join(d, "presets")) |
| os.environ.setdefault("F5_VOCAB", os.path.join(d, "vocab.txt")) |
| os.environ.setdefault("KRITI_ONNX", os.path.join(d, "kriti_onnx")) |
| os.environ.setdefault("KRITI_PY", "/nonexistent") |
| os.environ.setdefault("CONFORMER_PY", "/nonexistent") |
| os.environ.setdefault("BILINGUAL", "0") |
| import autoclone as A |
|
|
| VOICES = os.environ.get("VOICES_DIR", "/tmp/voices") |
| os.makedirs(VOICES, exist_ok=True) |
|
|
|
|
| def _presets(): |
| """{label: voice id} for the preset voices; their folders are copied next to the uploaded voices.""" |
| import shutil |
| src, out = os.environ.get("PRESETS_DIR", ""), {} |
| if not src or not os.path.isdir(src): |
| return out |
| meta = json.load(open(os.path.join(src, "voices.json"))) if os.path.exists(os.path.join(src, "voices.json")) else {} |
| for name in sorted(os.listdir(src)): |
| if os.path.isfile(os.path.join(src, name, "voice.json")): |
| if not os.path.exists(f"{VOICES}/{name}"): |
| shutil.copytree(os.path.join(src, name), f"{VOICES}/{name}") |
| out[f"{name.capitalize()} ({meta.get(name, {}).get('gender', 'voice')})"] = name |
| return out |
|
|
|
|
| PRESETS = _presets() |
|
|
|
|
| @gpu(duration=120) |
| def _enroll(vd, paths, same): |
| return A.enroll(vd, paths, one_speaker=same) |
|
|
|
|
| @gpu(duration=120) |
| def _say(vd, text, out, takes): |
| return A.say_long(vd, text, out, takes=takes) |
|
|
|
|
| def do_enroll(files, consent, same): |
| if not consent: |
| raise gr.Error("Please confirm the speaker agreed to be cloned.") |
| if not files: |
| raise gr.Error("Drop at least one clip (20 seconds or more of talking is best).") |
| paths = sorted(f if isinstance(f, str) else f.name for f in files) |
| vid = hashlib.sha1(("".join(open(p, "rb").read(1 << 20).hex()[:64] for p in paths) + str(same)).encode()).hexdigest()[:10] |
| vd = f"{VOICES}/{vid}" |
| v = json.load(open(f"{vd}/voice.json")) if os.path.exists(f"{vd}/voice.json") else _enroll(vd, paths, same) |
| main = [p for p in v["pieces"] if p["main"]] |
| sec = sum(p["speech"] for p in main) |
| msg = f"Voice ready: {sec:.0f} s of speech." + (" Tip: 20 seconds or more gives a closer voice." if sec < 15 else "") |
| if len(v["pieces"]) > len(main): |
| msg += " Some parts sounded like another person and were left out (tick the box if it is all one person)." |
| return vid, msg |
|
|
|
|
| def do_say(vid, text, quality): |
| if not vid: |
| raise gr.Error("Make the voice first (step 1).") |
| if not text.strip(): |
| raise gr.Error("Type something to say.") |
| if len(text) > 600: |
| raise gr.Error("Please keep it under 600 characters here.") |
| takes = {"Fast": 1, "Best": 4}[quality] |
| out = f"{VOICES}/{vid}/say_{hashlib.sha1((text + quality).encode()).hexdigest()[:8]}.wav" |
| b = _say(f"{VOICES}/{vid}", text, out, takes) |
| return out, f"Voice match {b['sim']:.2f}" |
|
|
|
|
| with gr.Blocks(title="Nepali voice cloning") as demo: |
| gr.Markdown("## Nepali voice cloning\n1. Drop 20-30 seconds of one person talking (a voice note is fine). " |
| "2. Type Nepali, English or both. 3. Listen. Only clone a voice with that person's permission.") |
| vid = gr.State("") |
| if PRESETS: |
| with gr.Row(): |
| pick = gr.Dropdown(list(PRESETS), label="Or pick one of our voices", value=None) |
| pinfo = gr.Textbox(label="Voice", interactive=False) |
| pick.change(lambda k: (PRESETS.get(k, ""), f"Using {k}." if k else ""), [pick], [vid, pinfo]) |
| with gr.Row(): |
| files = gr.File(file_count="multiple", file_types=["audio"], label="Clips of the voice") |
| with gr.Column(): |
| consent = gr.Checkbox(label="The speaker agreed to have their voice cloned") |
| same = gr.Checkbox(label="Every clip is this one person", value=True) |
| make = gr.Button("1. Make the voice", variant="primary") |
| info = gr.Textbox(label="Voice", interactive=False, lines=2) |
| text = gr.Textbox(label="2. Text", value="नमस्ते! आज office मा meeting धेरै लामो भयो।", lines=4) |
| quality = gr.Radio(["Fast", "Best"], value="Best", label="Quality") |
| go = gr.Button("3. Speak", variant="primary") |
| audio = gr.Audio(label="Result", type="filepath") |
| note = gr.Textbox(label="Check", interactive=False) |
| make.click(do_enroll, [files, consent, same], [vid, info]) |
| go.click(do_say, [vid, text, quality], [audio, note]) |
|
|
| if __name__ == "__main__": |
| demo.queue(default_concurrency_limit=1).launch() |
|
|
