app.py

import io
import re
import wave
import struct

import numpy as np
import torch
from fastapi import FastAPI, HTTPException
from fastapi.responses import StreamingResponse, Response, HTMLResponse
from fastapi.middleware import Middleware
from fastapi.middleware.gzip import GZipMiddleware

# --- IMPORTANT:  Use the AutoregressiveStreamKPipeline ---
from kokoro.pipeline import AutoregressiveStreamKPipeline  # Or wherever your pipeline is.

app = FastAPI(
    title="Kokoro TTS FastAPI",
    middleware=[
        Middleware(GZipMiddleware, compresslevel=9)  # Add GZip compression
    ]
)

# ------------------------------------------------------------------------------
# Global Pipeline Instance
# ------------------------------------------------------------------------------
# Create one pipeline instance for the entire app.

pipeline = AutoregressiveStreamKPipeline(lang_code="a")  # Use the autoregressive pipeline

# ------------------------------------------------------------------------------
# Helper Functions
# ------------------------------------------------------------------------------

def generate_wav_header(sample_rate: int, num_channels: int, sample_width: int, data_size: int = 0x7FFFFFFF) -> bytes:
    """
    Generate a WAV header for streaming.
    Since we don't know the final audio size, we set the data chunk size to a large dummy value.
    This header is sent only once at the start of the stream.
    """
    bits_per_sample = sample_width * 8
    byte_rate = sample_rate * num_channels * sample_width
    block_align = num_channels * sample_width
    # total file size = 36 + data_size (header is 44 bytes total)
    total_size = 36 + data_size
    header = struct.pack('<4sI4s', b'RIFF', total_size, b'WAVE')
    fmt_chunk = struct.pack('<4sIHHIIHH', b'fmt ', 16, 1, num_channels, sample_rate, byte_rate, block_align, bits_per_sample)
    data_chunk_header = struct.pack('<4sI', b'data', data_size)
    return header + fmt_chunk + data_chunk_header


def audio_tensor_to_pcm_bytes(audio_tensor: torch.Tensor) -> bytes:
    """
    Convert a torch.FloatTensor (with values in [-1, 1]) to raw 16-bit PCM bytes.
    """
    # Ensure tensor is on CPU and flatten if necessary.
    audio_np = audio_tensor.cpu().numpy()
    if audio_np.ndim > 1:
        audio_np = audio_np.flatten()
    # Scale to int16 range.
    audio_int16 = np.int16(audio_np * 32767)
    return audio_int16.tobytes()

def audio_tensor_to_opus_bytes(audio_tensor: torch.Tensor, sample_rate: int = 24000, bitrate: int = 32000) -> bytes:
    """
    Convert a torch.FloatTensor to Opus encoded bytes.
    Requires the 'opuslib' package: pip install opuslib
    """
    try:
        import opuslib
    except ImportError:
        raise ImportError("opuslib is not installed. Please install it with: pip install opuslib")

    audio_np = audio_tensor.cpu().numpy()
    if audio_np.ndim > 1:
        audio_np = audio_np.flatten()
    # Scale to int16 range.  Important for opus.
    audio_int16 = np.int16(audio_np * 32767)

    encoder = opuslib.Encoder(sample_rate, 1, opuslib.APPLICATION_VOIP) # 1 channel for mono.

    # Calculate the number of frames to encode. Opus frames are 2.5, 5, 10, or 20 ms long.
    frame_size = int(sample_rate * 0.020)  # 20ms frame size

    encoded_data = b''
    for i in range(0, len(audio_int16), frame_size):
        frame = audio_int16[i:i + frame_size]
        if len(frame) < frame_size:
            # Pad the last frame with zeros if needed.
            frame = np.pad(frame, (0, frame_size - len(frame)), 'constant')
        encoded_frame = encoder.encode(frame.tobytes(), frame_size) # Encode the frame.
        encoded_data += encoded_frame

    return encoded_data

# ------------------------------------------------------------------------------
# Endpoints
# ------------------------------------------------------------------------------

@app.get("/tts/streaming", summary="Streaming TTS (Autoregressive)")
def tts_streaming(text: str, voice: str = "af_heart", speed: float = 1.0, format: str = "opus"):
    """
    Streaming TTS endpoint that attempts autoregressive, near sample-by-sample output.

    IMPORTANT: This is EXPERIMENTAL and may have reduced quality compared to
    the full or chunking methods.  It's also likely to be slower due to the
    per-phoneme processing overhead.
    """
    sample_rate = 24000
    num_channels = 1
    sample_width = 2  # 16-bit PCM

    def audio_generator():
        if format.lower() == "wav":
            # Yield the WAV header first.
            header = generate_wav_header(sample_rate, num_channels, sample_width)
            yield header

        try:
            # Use the AUTOREGRESSIVE pipeline
            for audio_chunk in pipeline(text, voice=voice, speed=speed):
                if audio_chunk.numel() > 0:  # Ensure we have audio data
                    if format.lower() == "wav":
                        yield audio_tensor_to_pcm_bytes(audio_chunk)
                    elif format.lower() == "opus":
                        yield audio_tensor_to_opus_bytes(audio_chunk, sample_rate=sample_rate)
                    else:
                        raise ValueError(f"Unsupported audio format: {format}")

        except Exception as e:
            print(f"Error during streaming: {e}")
            yield b''  # Yield empty bytes to avoid breaking the stream

    media_type = "audio/wav" if format.lower() == "wav" else "audio/opus"
    return StreamingResponse(
        audio_generator(),
        media_type=media_type,
        headers={"Cache-Control": "no-cache"},
    )



@app.get("/tts/full", summary="Full TTS")
def tts_full(text: str, voice: str = "af_heart", speed: float = 1.0, format: str = "wav"):
    """
    Full TTS endpoint (no streaming).  Synthesizes the entire text and returns
    a complete WAV or Opus file.
    """
    # Use newline-based splitting.  This is the *original* KPipeline,
    # which is better for full synthesis.  It's important to use
    # the right pipeline for the right task.
    from kokoro.pipeline import KPipeline  # Import here to avoid circular import
    full_pipeline = KPipeline(lang_code="a")

    results = list(full_pipeline(text, voice=voice, speed=speed, split_pattern=r"\n+"))
    audio_segments = []
    for result in results:
        if result.audio is not None:
            audio_np = result.audio.cpu().numpy()
            if audio_np.ndim > 1:
                audio_np = audio_np.flatten()
            audio_segments.append(audio_np)

    if not audio_segments:
        raise HTTPException(status_code=500, detail="No audio generated.")

    # Concatenate all audio segments.
    full_audio = np.concatenate(audio_segments)

    # Write the concatenated audio to an in-memory WAV or Opus file.
    sample_rate = 24000
    num_channels = 1
    sample_width = 2  # 16-bit PCM -> 2 bytes per sample
    if format.lower() == "wav":
        wav_io = io.BytesIO()
        with wave.open(wav_io, "wb") as wav_file:
            wav_file.setnchannels(num_channels)
            wav_file.setsampwidth(sample_width)
            wav_file.setframerate(sample_rate)
            full_audio_int16 = np.int16(full_audio * 32767)
            wav_file.writeframes(full_audio_int16.tobytes())
        wav_io.seek(0)
        return Response(content=wav_io.read(), media_type="audio/wav")
    elif format.lower() == "opus":
        opus_data = audio_tensor_to_opus_bytes(torch.from_numpy(full_audio), sample_rate=sample_rate)
        return Response(content=opus_data, media_type="audio/opus")
    else:
        raise HTTPException(status_code=400, detail=f"Unsupported audio format: {format}")




@app.get("/", response_class=HTMLResponse)
def index():
    """
    HTML demo page for Kokoro TTS.
    """
    return """
<!DOCTYPE html>
<html>
<head>
    <title>Kokoro TTS Demo</title>
</head>
<body>
    <h1>Kokoro TTS Demo</h1>
    <textarea id="text" rows="4" cols="50" placeholder="Enter text here"></textarea><br>
    <label for="voice">Voice:</label>
    <input type="text" id="voice" value="af_heart"><br>
    <label for="speed">Speed:</label>
    <input type="number" step="0.1" id="speed" value="1.0"><br>
    <label for="format">Format:</label>
    <select id="format">
        <option value="wav">WAV</option>
        <option value="opus" selected>Opus</option>
    </select><br><br>
    <button onclick="playStreaming()">Play Streaming TTS</button>
    <button onclick="playFull()">Play Full TTS</button>
    <br><br>
    <audio id="audio" controls autoplay></audio>
    <script>
        function playStreaming() {
            const text = document.getElementById('text').value;
            const voice = document.getElementById('voice').value;
            const speed = document.getElementById('speed').value;
            const format = document.getElementById('format').value;
            const audio = document.getElementById('audio');
            // Set the audio element's source to the streaming endpoint.
            audio.src = `/tts/streaming?text=${encodeURIComponent(text)}&voice=${encodeURIComponent(voice)}&speed=${speed}&format=${format}`;
            audio.type = format === 'wav' ? 'audio/wav' : 'audio/opus';
            audio.play();
        }
        function playFull() {
            const text = document.getElementById('text').value;
            const voice = document.getElementById('voice').value;
            const speed = document.getElementById('speed').value;
            const format = document.getElementById('format').value;
            const audio = document.getElementById('audio');
            // Set the audio element's source to the full TTS endpoint.
            audio.src = `/tts/full?text=${encodeURIComponent(text)}&voice=${encodeURIComponent(voice)}&speed=${speed}&format=${format}`;
            audio.type = format === 'wav' ? 'audio/wav' : 'audio/opus';
            audio.play();
        }
    </script>
</body>
</html>
"""

# ------------------------------------------------------------------------------
# Run with: uvicorn app:app --reload
# ------------------------------------------------------------------------------

if __name__ == "__main__":
    import uvicorn

    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=True)