app.py

"""

TTS Hub: XTTS2 + Bark Text-to-Speech

CPU inference for HuggingFace Spaces free tier



Models:

- XTTS2: Voice cloning with reference audio (default)

- Bark: Preset voices with non-speech sounds [laughter], [music], etc.



Bark implementation matches original C0untFloyd/bark-gui features:

- Temperature controls (text_temp, waveform_temp)

- Text chunking for long inputs

- Seed control for reproducibility

"""
import argparse
import gc
import os
import re
import sys
import tempfile
from pathlib import Path

import numpy as np
import torch
import scipy.io.wavfile as wavfile

# Force CPU
os.environ["CUDA_VISIBLE_DEVICES"] = ""
os.environ["COQUI_TOS_AGREED"] = "1"
os.environ["SUNO_USE_SMALL_MODELS"] = "1"
DEVICE = "cpu"

# Bark sample rate (matches original)
BARK_SAMPLE_RATE = 24_000

# Global models (lazy loaded)
XTTS_MODEL = None
BARK_MODEL = None
BARK_PROCESSOR = None

# XTTS2 supported languages
XTTS_LANGUAGES = {
    "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": "zh-cn",
    "Japanese": "ja",
    "Korean": "ko",
    "Hungarian": "hu",
}

# Bark voice presets (matches original bark/generation.py ALLOWED_PROMPTS)
BARK_VOICES = [
    "v2/en_speaker_0",
    "v2/en_speaker_1",
    "v2/en_speaker_2",
    "v2/en_speaker_3",
    "v2/en_speaker_4",
    "v2/en_speaker_5",
    "v2/en_speaker_6",
    "v2/en_speaker_7",
    "v2/en_speaker_8",
    "v2/en_speaker_9",
    "v2/de_speaker_0",
    "v2/de_speaker_1",
    "v2/de_speaker_2",
    "v2/fr_speaker_0",
    "v2/fr_speaker_1",
    "v2/es_speaker_0",
    "v2/es_speaker_1",
    "v2/zh_speaker_0",
    "v2/zh_speaker_1",
    "v2/ja_speaker_0",
    "v2/ko_speaker_0",
]


def load_xtts():
    """Load XTTS2 model (lazy loading)"""
    global XTTS_MODEL
    if XTTS_MODEL is not None:
        return XTTS_MODEL

    print("Loading XTTS2 model...")
    from TTS.api import TTS

    XTTS_MODEL = TTS(model_name="tts_models/multilingual/multi-dataset/xtts_v2").to(DEVICE)
    gc.collect()
    print("XTTS2 loaded!")
    return XTTS_MODEL


def load_bark():
    """Load Bark model (lazy loading)"""
    global BARK_MODEL, BARK_PROCESSOR
    if BARK_MODEL is not None:
        return BARK_MODEL, BARK_PROCESSOR

    print("Loading Bark model...")
    from transformers import AutoProcessor, BarkModel

    BARK_PROCESSOR = AutoProcessor.from_pretrained("suno/bark-small")
    BARK_MODEL = BarkModel.from_pretrained("suno/bark-small").to(DEVICE)
    gc.collect()
    print("Bark loaded!")
    return BARK_MODEL, BARK_PROCESSOR


def split_and_recombine_text(text, desired_length=200, max_length=300):
    """

    Split text into chunks for processing.

    Matches original bark-gui/util/parseinput.py split_and_recombine_text()



    Args:

        text: Input text to split

        desired_length: Target chunk length

        max_length: Maximum chunk length



    Returns:

        List of text chunks

    """
    text = text.strip()
    if len(text) == 0:
        return []

    # Split on sentence boundaries
    sentences = re.split(r'(?<=[.!?])\s+', text)

    chunks = []
    current_chunk = ""

    for sentence in sentences:
        sentence = sentence.strip()
        if not sentence:
            continue

        # If adding this sentence exceeds max_length, save current chunk
        if len(current_chunk) + len(sentence) + 1 > max_length and current_chunk:
            chunks.append(current_chunk.strip())
            current_chunk = ""

        # If single sentence exceeds max_length, split by words
        if len(sentence) > max_length:
            words = sentence.split()
            for word in words:
                if len(current_chunk) + len(word) + 1 > max_length and current_chunk:
                    chunks.append(current_chunk.strip())
                    current_chunk = ""
                current_chunk += " " + word if current_chunk else word
        else:
            current_chunk += " " + sentence if current_chunk else sentence

        # If current chunk exceeds desired_length and ends with punctuation, save it
        if len(current_chunk) >= desired_length and current_chunk[-1] in '.!?':
            chunks.append(current_chunk.strip())
            current_chunk = ""

    # Add remaining text
    if current_chunk.strip():
        chunks.append(current_chunk.strip())

    return chunks if chunks else [text]


def generate_bark_audio(

    model,

    processor,

    text: str,

    voice_preset: str,

    text_temp: float = 0.7,

    waveform_temp: float = 0.7,

    seed: int = None,

) -> np.ndarray:
    """

    Generate audio for a single text chunk using Bark.

    Matches original bark-gui temperature and seed handling.



    Args:

        model: BarkModel instance

        processor: AutoProcessor instance

        text: Text to synthesize

        voice_preset: Voice preset string (e.g., "v2/en_speaker_6")

        text_temp: Semantic/text generation temperature (0.0-1.0)

        waveform_temp: Coarse/fine generation temperature (0.0-1.0)

        seed: Random seed for reproducibility (None = random)



    Returns:

        Audio array (numpy)

    """
    # Set seed for reproducibility (matches original pytorch_seed usage)
    if seed is not None and seed > 0:
        torch.manual_seed(seed)
        np.random.seed(seed)

    # Prepare inputs
    inputs = processor(text, voice_preset=voice_preset, return_tensors="pt").to(DEVICE)

    # Generate audio with temperature controls
    # Temperature kwargs passed directly to model.generate()
    with torch.no_grad():
        audio_array = model.generate(
            **inputs,
            do_sample=True,
            semantic_temperature=text_temp,
            coarse_temperature=waveform_temp,
            fine_temperature=0.5,
        )

    audio_array = audio_array.cpu().numpy().squeeze()
    return audio_array


def synthesize_xtts(

    text: str,

    reference_audio: str,

    language: str = "English",

    speed: float = 1.0,

    progress=None,

) -> tuple:
    """XTTS2 synthesis with voice cloning"""
    if not text or text.strip() == "":
        return None, "Please enter text to synthesize"
    if reference_audio is None:
        return None, "XTTS2 requires a reference voice audio file"

    if progress:
        progress(0.1, "Loading XTTS2...")

    tts = load_xtts()

    if progress:
        progress(0.3, "Processing...")

    lang_code = XTTS_LANGUAGES.get(language, "en")
    output_path = tempfile.mktemp(suffix=".wav")

    try:
        if progress:
            progress(0.5, "Generating speech (XTTS2)...")

        tts.tts_to_file(
            text=text,
            file_path=output_path,
            speaker_wav=reference_audio,
            language=lang_code,
            speed=speed,
        )

        if progress:
            progress(1.0, "Done!")

        gc.collect()
        return output_path, "XTTS2: Speech generated successfully!"

    except Exception as e:
        gc.collect()
        return None, f"XTTS2 Error: {str(e)}"


def synthesize_bark(

    text: str,

    voice_preset: str = "v2/en_speaker_6",

    text_temp: float = 0.7,

    waveform_temp: float = 0.7,

    seed: int = -1,

    progress=None,

) -> tuple:
    """

    Bark synthesis with preset voices.

    Matches original bark-gui features: temperature control, text chunking, seed.



    Args:

        text: Text to synthesize (can be long, will be chunked)

        voice_preset: Voice preset (e.g., "v2/en_speaker_6")

        text_temp: Text/semantic temperature (0.1-1.0, default 0.7)

        waveform_temp: Waveform/coarse temperature (0.1-1.0, default 0.7)

        seed: Random seed (-1 = random, >0 = fixed seed)

        progress: Gradio progress callback



    Returns:

        Tuple of (audio_path, status_message)

    """
    if not text or text.strip() == "":
        return None, "Please enter text to synthesize"

    if progress:
        progress(0.1, "Loading Bark...")

    model, processor = load_bark()

    if progress:
        progress(0.2, "Processing text...")

    # Handle seed (matches original: -1 or None = random)
    if seed is None or seed <= 0:
        seed = np.random.default_rng().integers(1, 2**32 - 1)

    # Clamp seed to valid range (matches original assertion)
    seed = int(seed) % (2**32 - 1)
    if seed <= 0:
        seed = 1

    output_path = tempfile.mktemp(suffix=".wav")

    try:
        # Split text into chunks (matches original split_and_recombine_text)
        text_chunks = split_and_recombine_text(text, desired_length=200, max_length=300)

        if progress:
            progress(0.3, f"Generating {len(text_chunks)} chunk(s)...")

        all_audio_parts = []

        # Silence between sentences (matches original: settings.silence_sentence)
        silence_samples = int(0.25 * BARK_SAMPLE_RATE)  # 250ms silence
        silence = np.zeros(silence_samples, dtype=np.float32)

        current_seed = seed

        for i, chunk in enumerate(text_chunks):
            if progress:
                pct = 0.3 + 0.6 * (i / len(text_chunks))
                progress(pct, f"Generating chunk {i+1}/{len(text_chunks)}...")

            # Generate audio for this chunk
            audio_array = generate_bark_audio(
                model=model,
                processor=processor,
                text=chunk,
                voice_preset=voice_preset,
                text_temp=text_temp,
                waveform_temp=waveform_temp,
                seed=current_seed,
            )

            all_audio_parts.append(audio_array)

            # Add silence between chunks (not after last)
            if i < len(text_chunks) - 1:
                all_audio_parts.append(silence)

            # Update seed for next chunk (matches original: currentseed = torch.random.initial_seed())
            current_seed = torch.random.initial_seed() % (2**32 - 1)
            if current_seed <= 0:
                current_seed = 1

        # Concatenate all audio parts
        final_audio = np.concatenate(all_audio_parts)

        # Save as WAV (matches original: write_wav(filename, SAMPLE_RATE, audio_array))
        wavfile.write(output_path, BARK_SAMPLE_RATE, final_audio)

        if progress:
            progress(1.0, "Done!")

        gc.collect()

        chunk_info = f" ({len(text_chunks)} chunks)" if len(text_chunks) > 1 else ""
        return output_path, f"Bark: Generated with {voice_preset}, seed={seed}{chunk_info}"

    except Exception as e:
        gc.collect()
        return None, f"Bark Error: {str(e)}"


def synthesize(

    text: str,

    model_choice: str = "XTTS2 (Voice Cloning)",

    reference_audio: str = None,

    language: str = "English",

    speed: float = 1.0,

    voice_preset: str = "v2/en_speaker_6",

    text_temp: float = 0.7,

    waveform_temp: float = 0.7,

    seed: int = -1,

    progress=None,

) -> tuple:
    """

    Unified TTS synthesis.



    Args:

        text: Text to convert to speech

        model_choice: "XTTS2 (Voice Cloning)" or "Bark (Preset Voices)"

        reference_audio: Reference audio for XTTS2 voice cloning

        language: Target language (XTTS2 only)

        speed: Speech speed (XTTS2 only)

        voice_preset: Bark voice preset

        text_temp: Bark text/semantic temperature (0.1-1.0)

        waveform_temp: Bark waveform/coarse temperature (0.1-1.0)

        seed: Bark random seed (-1 = random)



    Returns:

        Tuple of (audio_path, status_message)

    """
    if "XTTS2" in model_choice:
        return synthesize_xtts(text, reference_audio, language, speed, progress)
    else:
        return synthesize_bark(text, voice_preset, text_temp, waveform_temp, seed, progress)


def cli_synthesize(args):
    """CLI mode for synthesis"""
    if args.model == "xtts2":
        if not args.reference:
            print("Error: XTTS2 requires --reference audio file")
            sys.exit(1)

        from TTS.api import TTS

        print("Loading XTTS2 model...")
        tts = TTS(model_name="tts_models/multilingual/multi-dataset/xtts_v2").to(DEVICE)

        lang_code = XTTS_LANGUAGES.get(args.language, "en")

        print(f"Text: {args.text[:100]}{'...' if len(args.text) > 100 else ''}")
        print(f"Reference: {args.reference}")
        print(f"Language: {args.language} ({lang_code})")
        print(f"Speed: {args.speed}")
        print("Generating speech...")

        tts.tts_to_file(
            text=args.text,
            file_path=args.output,
            speaker_wav=args.reference,
            language=lang_code,
            speed=args.speed,
        )

    else:  # bark
        print("Loading Bark model...")
        model, processor = load_bark()

        # Handle seed
        seed = args.seed
        if seed <= 0:
            seed = np.random.default_rng().integers(1, 2**32 - 1)
        seed = int(seed) % (2**32 - 1)

        print(f"Text: {args.text[:100]}{'...' if len(args.text) > 100 else ''}")
        print(f"Voice: {args.voice}")
        print(f"Text temp: {args.text_temp}")
        print(f"Waveform temp: {args.waveform_temp}")
        print(f"Seed: {seed}")

        # Split text into chunks
        text_chunks = split_and_recombine_text(args.text, desired_length=200, max_length=300)
        print(f"Processing {len(text_chunks)} chunk(s)...")

        all_audio_parts = []
        silence = np.zeros(int(0.25 * BARK_SAMPLE_RATE), dtype=np.float32)
        current_seed = seed

        for i, chunk in enumerate(text_chunks):
            print(f"  Chunk {i+1}/{len(text_chunks)}: {chunk[:50]}...")

            audio_array = generate_bark_audio(
                model=model,
                processor=processor,
                text=chunk,
                voice_preset=args.voice,
                text_temp=args.text_temp,
                waveform_temp=args.waveform_temp,
                seed=current_seed,
            )

            all_audio_parts.append(audio_array)
            if i < len(text_chunks) - 1:
                all_audio_parts.append(silence)

            current_seed = torch.random.initial_seed() % (2**32 - 1)

        final_audio = np.concatenate(all_audio_parts)
        wavfile.write(args.output, BARK_SAMPLE_RATE, final_audio)

    print(f"Output saved to: {args.output}")


def launch_gradio():
    """Launch Gradio UI"""
    import gradio as gr

    description = """

# TTS Hub: XTTS2 + Bark



Two powerful TTS models in one space:



| Model | Voice Source | Special Features |

|-------|--------------|------------------|

| **XTTS2** (default) | Your audio sample | Voice cloning, 16 languages |

| **Bark** | Preset voices | [laughter], [music], temperature control, seed |



**Bark special tokens:** `[laughter]` `[laughs]` `[sighs]` `[music]` `[gasps]` `[clears throat]` `♪ singing ♪`

"""

    with gr.Blocks(title="TTS Hub") as demo:
        gr.Markdown(description)

        with gr.Row():
            with gr.Column():
                text_input = gr.Textbox(
                    label="Text to synthesize",
                    placeholder="Enter text... Use [laughter] [music] for Bark. Long text will be automatically chunked.",
                    lines=4,
                    value="Hello! This is a test of the text to speech system.",
                )

                model_choice = gr.Radio(
                    choices=["XTTS2 (Voice Cloning)", "Bark (Preset Voices)"],
                    value="XTTS2 (Voice Cloning)",
                    label="Model",
                )

                # XTTS2 options
                with gr.Group(visible=True) as xtts_options:
                    reference_audio = gr.Audio(
                        label="Reference Voice (3-30 seconds)",
                        type="filepath",
                    )
                    language = gr.Dropdown(
                        choices=list(XTTS_LANGUAGES.keys()),
                        value="English",
                        label="Language",
                    )
                    speed = gr.Slider(
                        minimum=0.5,
                        maximum=2.0,
                        value=1.0,
                        step=0.1,
                        label="Speed",
                    )

                # Bark options (matches original bark-gui controls)
                with gr.Group(visible=False) as bark_options:
                    voice_preset = gr.Dropdown(
                        choices=BARK_VOICES,
                        value="v2/en_speaker_6",
                        label="Voice Preset",
                    )
                    with gr.Row():
                        text_temp = gr.Slider(
                            minimum=0.1,
                            maximum=1.0,
                            value=0.7,
                            step=0.1,
                            label="Text Temperature",
                            info="Higher = more diverse, lower = more conservative"
                        )
                        waveform_temp = gr.Slider(
                            minimum=0.1,
                            maximum=1.0,
                            value=0.7,
                            step=0.1,
                            label="Waveform Temperature",
                            info="Higher = more diverse, lower = more conservative"
                        )
                    seed = gr.Number(
                        value=-1,
                        label="Seed",
                        info="-1 = random, positive number = reproducible",
                        precision=0,
                    )
                    gr.Markdown("""

**Voice codes:** `en`=English, `de`=German, `fr`=French, `es`=Spanish, `zh`=Chinese, `ja`=Japanese, `ko`=Korean

""")

                generate_btn = gr.Button("Generate Speech", variant="primary", size="lg")

            with gr.Column():
                output_audio = gr.Audio(label="Generated Speech")
                status = gr.Textbox(label="Status", interactive=False)

        # Toggle visibility based on model choice
        def toggle_options(choice):
            if "XTTS2" in choice:
                return gr.update(visible=True), gr.update(visible=False)
            else:
                return gr.update(visible=False), gr.update(visible=True)

        model_choice.change(
            fn=toggle_options,
            inputs=[model_choice],
            outputs=[xtts_options, bark_options],
        )

        generate_btn.click(
            fn=synthesize,
            inputs=[text_input, model_choice, reference_audio, language, speed,
                    voice_preset, text_temp, waveform_temp, seed],
            outputs=[output_audio, status],
            api_name="synthesize",
        )

        gr.Markdown("""

## Tips

- **XTTS2:** Use 3-30 seconds of clear speech as reference. No background noise.

- **Bark:** Long text is automatically split into chunks. Use temperature controls to adjust output diversity.

- **Bark Seed:** Use a positive seed number to get reproducible results.

- **CPU Speed:** XTTS2 ~1-2 min, Bark ~30-60 sec per chunk.

""")

    demo.queue().launch()


def main():
    parser = argparse.ArgumentParser(description="TTS Hub: XTTS2 + Bark")
    subparsers = parser.add_subparsers(dest="command")

    # TTS command
    tts_parser = subparsers.add_parser("tts", help="Text-to-speech synthesis")
    tts_parser.add_argument("-t", "--text", required=True, help="Text to synthesize")
    tts_parser.add_argument("-o", "--output", required=True, help="Output audio path")
    tts_parser.add_argument(
        "-m", "--model",
        default="xtts2",
        choices=["xtts2", "bark"],
        help="TTS model (default: xtts2)"
    )
    # XTTS2 options
    tts_parser.add_argument("-r", "--reference", help="Reference voice audio (XTTS2)")
    tts_parser.add_argument("-l", "--language", default="English", help="Language (XTTS2)")
    tts_parser.add_argument("-s", "--speed", type=float, default=1.0, help="Speed (XTTS2)")
    # Bark options (matches original bark-gui)
    tts_parser.add_argument(
        "-v", "--voice",
        default="v2/en_speaker_6",
        help="Voice preset (Bark)"
    )
    tts_parser.add_argument(
        "--text-temp",
        type=float,
        default=0.7,
        help="Text/semantic temperature 0.1-1.0 (Bark, default: 0.7)"
    )
    tts_parser.add_argument(
        "--waveform-temp",
        type=float,
        default=0.7,
        help="Waveform/coarse temperature 0.1-1.0 (Bark, default: 0.7)"
    )
    tts_parser.add_argument(
        "--seed",
        type=int,
        default=-1,
        help="Random seed, -1=random (Bark)"
    )

    args = parser.parse_args()

    if args.command == "tts":
        cli_synthesize(args)
    else:
        launch_gradio()


if __name__ == "__main__":
    if len(sys.argv) > 1:
        main()
    else:
        launch_gradio()