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Running on Zero
Running on Zero
| """Instant Voice Cloning (F5-TTS / Chatterbox) & Local ZeroGPU Neural TTS Studio.""" | |
| import os | |
| import shutil | |
| import tempfile | |
| os.environ.setdefault("PYTORCH_ALLOC_CONF", "backend:cudaMallocAsync") | |
| os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "backend:cudaMallocAsync") | |
| import spaces # Must be imported before torch | |
| import gradio as gr | |
| import numpy as np | |
| import scipy.io.wavfile as wavfile | |
| import torch | |
| from gradio_client import Client, handle_file | |
| from transformers import AutoProcessor, BarkModel | |
| HF_TOKEN = os.environ.get("HF_TOKEN") | |
| LOCAL_TTS_ID = "suno/bark-small" | |
| VOICE_PRESETS = { | |
| "English Narrator · Studio Male (v2/en_speaker_6)": "v2/en_speaker_6", | |
| "English Narrator · Warm Female (v2/en_speaker_9)": "v2/en_speaker_9", | |
| "English Broadcast · Deep Male (v2/en_speaker_0)": "v2/en_speaker_0", | |
| "English Conversational · Male (v2/en_speaker_3)": "v2/en_speaker_3", | |
| } | |
| print(f"[voice-clone-studio] Preloading {LOCAL_TTS_ID} at module scope for ZeroGPU...", flush=True) | |
| processor = AutoProcessor.from_pretrained(LOCAL_TTS_ID) | |
| bark_model = BarkModel.from_pretrained(LOCAL_TTS_ID, torch_dtype=torch.float16) | |
| bark_model.to("cuda") | |
| SAMPLE_RATE = bark_model.generation_config.sample_rate | |
| print("[voice-clone-studio] Local ZeroGPU TTS ready!", flush=True) | |
| def synthesize_local_tts(text: str, voice_preset_key: str) -> tuple[str, str]: | |
| """Generate expressive speech locally on ZeroGPU using Bark.""" | |
| preset = VOICE_PRESETS.get(voice_preset_key, "v2/en_speaker_6") | |
| inputs = processor(text.strip(), voice_preset=preset).to("cuda") | |
| with torch.no_grad(): | |
| audio_array = bark_model.generate(**inputs) | |
| audio_np = audio_array.cpu().numpy().squeeze().astype(np.float32) | |
| with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp: | |
| wavfile.write(tmp.name, rate=SAMPLE_RATE, data=audio_np) | |
| return tmp.name, f"Local ZeroGPU Neural TTS ({preset} · {SAMPLE_RATE} Hz)" | |
| def clone_or_speak( | |
| text_to_speak: str, | |
| reference_audio_path: str | None = None, | |
| reference_transcript: str = "", | |
| voice_preset_key: str = "English Narrator · Studio Male (v2/en_speaker_6)", | |
| ) -> tuple[str, str]: | |
| """Clone a voice from a 5-10s audio clip (via F5-TTS ZeroGPU) or generate studio neural narration.""" | |
| if not text_to_speak or not text_to_speak.strip(): | |
| raise gr.Error("Please enter the text you want spoken.") | |
| if reference_audio_path: | |
| for space_id in ("mrfakename/E2-F5-TTS", "SWivid/F5-TTS"): | |
| try: | |
| client = Client(space_id, token=HF_TOKEN, httpx_kwargs={"timeout": 180.0}) | |
| res = client.predict( | |
| handle_file(reference_audio_path), | |
| (reference_transcript or "").strip(), | |
| text_to_speak.strip(), | |
| False, | |
| api_name="/predict", | |
| ) | |
| wav_out = res[0] if isinstance(res, (list, tuple)) else res | |
| with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp: | |
| shutil.copyfile(wav_out, tmp.name) | |
| return tmp.name, f"Zero-Shot Voice Clone via {space_id}" | |
| except Exception as err: # noqa: BLE001 | |
| print(f"[voice-clone-studio] {space_id} fallback ({err!r})", flush=True) | |
| return synthesize_local_tts(text_to_speak, voice_preset_key) | |
| with gr.Blocks(title="Instant Voice Cloning & TTS Studio", fill_width=True) as demo: | |
| gr.Markdown( | |
| "# 🎙️ Instant Voice Cloning & Neural TTS Studio\n" | |
| "Upload a **5–10 second voice clip** for **Zero-Shot Voice Cloning (F5-TTS)**, or leave the reference audio blank " | |
| "to generate studio narration on **ZeroGPU (`zero-a10g`)** at **$0/hour**." | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| text_box = gr.Textbox( | |
| label="Script / Text to Speak", | |
| lines=5, | |
| placeholder="Type the narration, dialogue, or voiceover script here...", | |
| ) | |
| ref_audio = gr.Audio( | |
| label="Reference Voice Audio (Optional · 5–10s clip for Voice Cloning)", | |
| type="filepath", | |
| ) | |
| ref_text = gr.Textbox( | |
| label="Reference Audio Transcript (Optional · improves F5-TTS voice clone accuracy)", | |
| lines=2, | |
| placeholder="Leave blank for automatic transcription, or type what was said in the reference clip...", | |
| ) | |
| preset_dropdown = gr.Dropdown( | |
| choices=list(VOICE_PRESETS), | |
| value="English Narrator · Studio Male (v2/en_speaker_6)", | |
| label="Studio Voice Preset (Used when no reference audio is uploaded)", | |
| ) | |
| speak_btn = gr.Button("🎙️ Generate Voiceover", variant="primary") | |
| with gr.Column(scale=1): | |
| audio_out = gr.Audio(label="Generated Voiceover (WAV)", type="filepath", autoplay=True) | |
| status_out = gr.Textbox(label="Engine Details", interactive=False) | |
| speak_btn.click( | |
| fn=clone_or_speak, | |
| inputs=[text_box, ref_audio, ref_text, preset_dropdown], | |
| outputs=[audio_out, status_out], | |
| api_name="synthesize_voice", | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(theme=gr.themes.Citrus(), mcp_server=True) | |