import io
import re
import wave
import struct
import time
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
from kokoro import KPipeline, StreamKPipeline
from kokoro.model import KModel
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.
model = KModel() # Or however you initialize/load your model
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
#pipeline = KPipeline(lang_code="a",model=model)
voice = "af_heart"
speed = 1.0
pipeline = StreamKPipeline(lang_code="a", model=model, voice=voice, device=device, speed=speed)
# ------------------------------------------------------------------------------
# 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()
# ------------------------------------------------------------------------------
# Endpoints
# ------------------------------------------------------------------------------
@app.get("/tts/streaming", summary="Streaming TTS")
def tts_streaming(text: str):
"""
Streaming TTS endpoint that returns a continuous audio stream.
The endpoint yields a WAV header (with a dummy length) for WAV,
then yields encoded audio data for each phoneme as soon as it is generated.
"""
sample_rate = 24000
num_channels = 1
sample_width = 2 # 16-bit PCM
def audio_generator():
# Yield the WAV header first.
header = generate_wav_header(sample_rate, num_channels, sample_width)
yield header
# Process and yield each audio chunk.
try:
for result in pipeline(text): # Use StreamKPipeline
if result.audio is not None:
yield audio_tensor_to_pcm_bytes(result.audio)
else:
print("No audio generated for phoneme")
except Exception as e:
print(f"Error processing: {e}")
yield b'' # Important so that streaming continues.
media_type = "audio/wav"
return StreamingResponse(
audio_generator(),
media_type=media_type,
headers={"Cache-Control": "no-cache"},
)
#Remove full tts
@app.get("/", response_class=HTMLResponse)
def index():
"""
HTML demo page for Kokoro TTS.
This page provides a simple UI to enter text and play synthesized audio from the streaming endpoint.
"""
return """
Kokoro TTS Demo