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Merge pull request #145 from remsky/revert-92-v0.1.2-pre

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Revert "Adds support for creating weighted voice combinations" (Implemented somewhat differently)
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remsky 2025-02-09 22:42:43 -07:00 • committed by GitHub
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7 changed files with 35 additions and 300 deletions
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100
api/src/inference/voice_manager.py
View file

@ -182,106 +182,6 @@ class VoiceManager:
except Exception:
return False
async def create_weighted_voice(
self,
formula: str,
normalize: bool = False,
device: str = "cpu",
) -> str:
"""
Parse the voice formula string (e.g. '0.3 * voiceA + 0.5 * voiceB')
and return a combined torch.Tensor representing the weighted sum
of the given voices.
Args:
formula: Weighted voice formula string.
voice_manager: A class that has a `load_voice(voice_name, device)` -> Tensor
device: 'cpu' or 'cuda' for the final tensor.
normalize: If True, divide the final result by the sum of the weights
so the total "magnitude" remains consistent.
Returns:
A torch.Tensor containing the combined voice embedding.
"""
pairs = self.parse_voice_formula(formula) # [(weight, voiceName), (weight, voiceName), ...]
# Validate the pairs
for weight, voice_name in pairs:
if weight <= 0:
raise ValueError(f"Invalid weight {weight} for voice {voice_name}.")
if not pairs:
raise ValueError("No valid weighted voices found in formula.")
# Keep track of total weight if we plan to normalize.
total_weight = 0.0
weighted_sum = None
combined_name = ""
for weight, voice_name in pairs:
# 1) Load each base voice from your manager/service
base_voice = await self.load_voice(voice_name, device=device)
# 3) Combine the base voices using the weights
if combined_name == "":
combined_name = voice_name
else:
combined_name += f"+{voice_name}"
# 2) Multiply by weight and accumulate
if weighted_sum is None:
# Clone so we don't modify the base voice in memory
weighted_sum = base_voice.clone() * weight
else:
weighted_sum += (base_voice * weight)
total_weight += weight
if weighted_sum is None:
raise ValueError("No voices were combined. Check the formula syntax.")
# Optional normalization
if normalize and total_weight != 0.0:
weighted_sum /= total_weight
if settings.allow_local_voice_saving:
# Save to disk
api_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
voices_dir = os.path.join(api_dir, settings.voices_dir)
os.makedirs(voices_dir, exist_ok=True)
combined_path = os.path.join(voices_dir, f"{formula}.pt")
try:
torch.save(weighted_sum, combined_path)
except Exception as e:
logger.warning(f"Failed to save combined voice: {e}")
# Continue without saving - will be combined on-the-fly when needed
return combined_name
def parse_voice_formula(self,formula: str) -> List[tuple[float, str]]:
"""
Parse the voice formula string (e.g. '0.3 * voiceA + 0.5 * voiceB')
and return a list of (weight, voiceName) pairs.
Args:
formula: Weighted voice formula string.
Returns:
List of (weight, voiceName) pairs.
"""
pairs = []
parts = formula.split('+')
for part in parts:
weight, voice_name = part.strip().split('*')
pairs.append((float(weight), voice_name.strip()))
return pairs
@property
def cache_info(self) -> Dict[str, int]:
"""Get cache statistics.

47
api/src/routers/openai_compatible.py
View file

@ -2,7 +2,7 @@ import json
import os
from typing import AsyncGenerator, Dict, List, Union
from fastapi import APIRouter, Header, HTTPException, Request, Response
from fastapi import APIRouter, Depends, Header, HTTPException, Request, Response
from fastapi.responses import StreamingResponse
from loguru import logger
@ -112,17 +112,7 @@ async def stream_audio_chunks(
client_request: Request
) -> AsyncGenerator[bytes, None]:
"""Stream audio chunks as they're generated with client disconnect handling"""
# Check if 'request.voice' is a weighted formula (contains '*')
if '*' in request.voice:
# Weighted formula path
voice_to_use = await tts_service._voice_manager.create_weighted_voice(
formula=request.voice,
normalize=True
)
else:
# Normal single or multi-voice path
voice_to_use = await process_voices(request.voice, tts_service)
voice_to_use = await process_voices(request.voice, tts_service)
try:
async for chunk in tts_service.generate_audio_stream(
@ -169,7 +159,10 @@ async def create_speech(
# Get global service instance
tts_service = await get_tts_service()
# Set content type based on format
# Process voice combination and validate
voice_to_use = await process_voices(request.voice, tts_service)
# Set content type based on format
content_type = {
"mp3": "audio/mpeg",
"opus": "audio/opus",
@ -216,27 +209,13 @@ async def create_speech(
},
)
else:
# Check if 'request.voice' is a weighted formula (contains '*')
if '*' in request.voice:
# Weighted formula path
print("Weighted formula path")
voice_to_use = await tts_service._voice_manager.create_weighted_voice(
formula=request.voice,
normalize=True
)
print(voice_to_use)
else:
# Normal single or multi-voice path
print("Normal single or multi-voice path")
# Otherwise, handle normal single or multi-voice logic
voice_to_use = await process_voices(request.voice, tts_service)
# Generate complete audio using public interface
audio, _ = await tts_service.generate_audio(
text=request.input,
voice=voice_to_use,
speed=request.speed,
stitch_long_output=True
)
# Generate complete audio using public interface
audio, _ = await tts_service.generate_audio(
text=request.input,
voice=voice_to_use,
speed=request.speed,
stitch_long_output=True
)
# Convert to requested format
content = await AudioService.convert_audio(

77
docker/gpu/docker-compose.yml
View file

@ -1,6 +1,7 @@
name: InstaVoice
name: kokoro-tts
services:
server1:
kokoro-tts:
# image: ghcr.io/remsky/kokoro-fastapi-gpu:v0.1.0
build:
context: ../..
dockerfile: docker/gpu/Dockerfile
@ -18,59 +19,23 @@ services:
reservations:
devices:
- driver: nvidia
count: all
count: 1
capabilities: [gpu]
server2:
build:
context: ../..
dockerfile: docker/gpu/Dockerfile
volumes:
- ../../api:/app/api
ports:
- "8880:8880"
environment:
- PYTHONPATH=/app:/app/api
- USE_GPU=true
- USE_ONNX=false
- PYTHONUNBUFFERED=1
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: all
capabilities: [gpu]
server3:
build:
context: ../..
dockerfile: docker/gpu/Dockerfile
volumes:
- ../../api:/app/api
ports:
- "8880:8880"
environment:
- PYTHONPATH=/app:/app/api
- USE_GPU=true
- USE_ONNX=false
- PYTHONUNBUFFERED=1
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: all
capabilities: [gpu]
nginx:
image: nginx:alpine
ports:
- "80:80" # Expose port 80 on the host machine
volumes:
- ./nginx.conf:/etc/nginx/nginx.conf # Load custom NGINX configuration
depends_on:
- server3
- server1
- server2
# # Gradio UI service
# gradio-ui:
# image: ghcr.io/remsky/kokoro-fastapi-ui:v0.1.0
# # Uncomment below to build from source instead of using the released image
# # build:
# # context: ../../ui
# ports:
# - "7860:7860"
# volumes:
# - ../../ui/data:/app/ui/data
# - ../../ui/app.py:/app/app.py # Mount app.py for hot reload
# environment:
# - GRADIO_WATCH=1 # Enable hot reloading
# - PYTHONUNBUFFERED=1 # Ensure Python output is not buffered
# - DISABLE_LOCAL_SAVING=false # Set to 'true' to disable local saving and hide file view
# - API_HOST=kokoro-tts # Set TTS service URL
# - API_PORT=8880 # Set TTS service PORT

78
docker/gpu/nginx.conf
View file

@ -1,78 +0,0 @@
user nginx;
worker_processes auto; # Automatically adjust worker processes based on available CPUs
events {
worker_connections 1024; # Maximum simultaneous connections per worker
use epoll; # Use efficient event handling for Linux
}
http {
# Basic security headers
add_header X-Frame-Options SAMEORIGIN always; # Prevent clickjacking
add_header X-Content-Type-Options nosniff always; # Prevent MIME-type sniffing
add_header X-XSS-Protection "1; mode=block" always; # Enable XSS protection in browsers
add_header Strict-Transport-Security "max-age=31536000; includeSubDomains" always; # Enforce HTTPS
add_header Content-Security-Policy "default-src 'self';" always; # Restrict resource loading to same origin
# Timeouts
sendfile on; # Enable sendfile for efficient file serving
tcp_nopush on; # Reduce packet overhead
tcp_nodelay on; # Minimize latency
keepalive_timeout 65; # Keep connections alive for 65 seconds
client_max_body_size 10m; # Limit request body size to 10MB
client_body_timeout 12; # Timeout for client body read
client_header_timeout 12; # Timeout for client header read
# Compression
gzip on; # Enable gzip compression
gzip_disable "msie6"; # Disable gzip for old browsers
gzip_vary on; # Add "Vary: Accept-Encoding" header
gzip_proxied any; # Enable gzip for proxied requests
gzip_comp_level 6; # Compression level
gzip_types text/plain text/css application/json application/javascript text/xml application/xml application/xml+rss text/javascript;
# Load balancing upstream
upstream backend {
least_conn; # Use least connections load balancing strategy
server server1:8880 max_fails=3 fail_timeout=5s; # Add health check for backend servers
# Uncomment additional servers for scaling:
server server2:8880 max_fails=3 fail_timeout=5s;
server server3:8880 max_fails=3 fail_timeout=5s;
}
server {
listen 80;
# Redirect HTTP to HTTPS (optional)
# Uncomment the lines below if SSL is configured:
# listen 443 ssl;
# ssl_certificate /path/to/certificate.crt;
# ssl_certificate_key /path/to/private.key;
location / {
proxy_pass http://backend; # Proxy traffic to the backend servers
proxy_http_version 1.1; # Use HTTP/1.1 for persistent connections
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "upgrade";
proxy_set_header Host $host;
proxy_set_header X-Forwarded-For $remote_addr; # Forward client IP
proxy_cache_bypass $http_upgrade;
proxy_read_timeout 60s; # Adjust read timeout for backend
proxy_connect_timeout 60s; # Adjust connection timeout for backend
proxy_send_timeout 60s; # Adjust send timeout for backend
}
# Custom error pages
error_page 502 503 504 /50x.html;
location = /50x.html {
root /usr/share/nginx/html;
}
# Deny access to hidden files (e.g., .git)
location ~ /\. {
deny all;
access_log off;
log_not_found off;
}
}
}

2
examples/openai_streaming_audio.py
View file

@ -31,7 +31,7 @@ def stream_to_speakers() -> None:
with openai.audio.speech.with_streaming_response.create(
model="kokoro",
voice="0.100 * af + 0.300 * am_adam + 0.400 * am_michael + 0.100 * bf_emma + 0.100 * bm_lewis ",
voice="af_bella",
response_format="pcm", # similar to WAV, but without a header chunk at the start.
input="""I see skies of blue and clouds of white
The bright blessed days, the dark sacred nights

BIN
examples/speech.mp3
View file

Binary file not shown.

31
setup.sh
View file

@ -1,31 +0,0 @@
#!/bin/bash
# Ensure models directory exists
mkdir -p api/src/models
# Function to download a file
download_file() {
local url="$1"
local filename=$(basename "$url")
echo "Downloading $filename..."
curl -L "$url" -o "api/src/models/$filename"
}
# Default PTH model if no arguments provided
DEFAULT_MODELS=(
"https://github.com/remsky/Kokoro-FastAPI/releases/download/v0.1.0/kokoro-v0_19.pth"
)
# Use provided models or default
if [ $# -gt 0 ]; then
MODELS=("$@")
else
MODELS=("${DEFAULT_MODELS[@]}")
fi
# Download all models
for model in "${MODELS[@]}"; do
download_file "$model"
done
echo "PyTorch model download complete!"