Deploy voice-clone-studio on ZeroGPU

README.md

@@ -1,13 +1,26 @@
 ---
-title: Voice Clone Studio
-emoji: 🐢
-colorFrom: green
-colorTo: indigo
+title: Voice Cloning & Neural TTS Studio
+emoji: 🎙️
+colorFrom: purple
+colorTo: blue
 sdk: gradio
-sdk_version: 6.31.0
-python_version: '3.12'
+sdk_version: 6.13.0
 app_file: app.py
-pinned: false
+python_version: "3.12"
+startup_duration_timeout: 1h
+short_description: Zero-Shot Voice Cloning (F5-TTS/Chatterbox) & Neural TTS
+models:
+  - SWivid/F5-TTS
+  - suno/bark-small
+tags:
+  - voice-cloning
+  - tts
+  - text-to-speech
+  - f5-tts
+  - zerogpu
+  - mcp-server
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🎙️ Instant Voice Cloning & Neural TTS Studio
+
+Upload a **5–10 second reference audio clip** to clone any voice via **F5-TTS / Chatterbox ZeroGPU**, or generate expressive multi-speaker neural voiceovers locally on **ZeroGPU (`zero-a10g`)** at **$0/hour**.

app.py

@@ -0,0 +1,120 @@
+"""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)
+
+
+@spaces.GPU(duration=45)
+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)

requirements.txt

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+transformers>=4.49.0
+accelerate>=1.6.0
+safetensors
+scipy
+numpy
+gradio_client
+soundfile