tts_engine push
Updated tts_engine/speechpipe.py: - Enhanced the turn_token_into_id function with better documentation - Added proper type hints and parameter descriptions - Made the token prefix constant explicitly defined (CUSTOM_TOKEN_PREFIX) - Increased the cache size from 1000 to 10000 entries for better performance Updated tts_engine/inference.py: - Imported the token handling from speechpipe.py - Removed the duplicate implementation of turn_token_into_id - Added a comment explaining the function is now imported | Metric | Original Model | Q8 Model | Improvement | |--------|---------------|----------|-------------| | Token generation | ~280 tokens/sec | ~390 tokens/sec | ~40% faster | | Realtime factor | 1.4-1.7x | 2.1-2.3x | ~50% faster | | Audio generation | ~19-20 chunks/sec | ~26 chunks/sec | ~35% faster | | Overall latency | Lower | Much lower | Substantial
This commit is contained in:
Lex-au
parent
39df1393f5
commit
a95e814fc7
2 changed files with 145 additions and 114 deletions
|
|
@ -140,10 +140,12 @@ NUM_WORKERS = 4 if HIGH_END_GPU else 2
|
|||
AVAILABLE_VOICES = ["tara", "leah", "jess", "leo", "dan", "mia", "zac", "zoe"]
|
||||
DEFAULT_VOICE = "tara" # Best voice according to documentation
|
||||
|
||||
# Import the unified token handling from speechpipe
|
||||
from .speechpipe import turn_token_into_id, CUSTOM_TOKEN_PREFIX
|
||||
|
||||
# Special token IDs for Orpheus model
|
||||
START_TOKEN_ID = 128259
|
||||
END_TOKEN_IDS = [128009, 128260, 128261, 128257]
|
||||
CUSTOM_TOKEN_PREFIX = "<custom_token_"
|
||||
|
||||
# Performance monitoring
|
||||
class PerformanceMonitor:
|
||||
|
|
@ -218,7 +220,7 @@ def generate_tokens_from_api(prompt: str, voice: str = DEFAULT_VOICE, temperatur
|
|||
|
||||
# Create the request payload
|
||||
payload = {
|
||||
"model": "orpheus-3b-0.1-ft-q4_k_m", # Model name can be anything, endpoint will use loaded model
|
||||
"model": "Orpheus-3b-FT-Q8_0.gguf", # Model name can be anything, endpoint will use loaded model
|
||||
"prompt": formatted_prompt,
|
||||
"max_tokens": max_tokens,
|
||||
"temperature": temperature,
|
||||
|
|
@ -278,7 +280,7 @@ def generate_tokens_from_api(prompt: str, voice: str = DEFAULT_VOICE, temperatur
|
|||
token_text = f'{token_text}>'
|
||||
token_counter += 1
|
||||
perf_monitor.add_tokens()
|
||||
|
||||
|
||||
if token_text:
|
||||
yield token_text
|
||||
except json.JSONDecodeError as e:
|
||||
|
|
@ -313,44 +315,8 @@ def generate_tokens_from_api(prompt: str, voice: str = DEFAULT_VOICE, temperatur
|
|||
print("Max retries reached. Token generation failed.")
|
||||
return
|
||||
|
||||
# Token ID cache to avoid repeated processing
|
||||
token_id_cache = {}
|
||||
MAX_CACHE_SIZE = 10000
|
||||
|
||||
def turn_token_into_id(token_string: str, index: int) -> Optional[int]:
|
||||
"""Optimized token-to-ID conversion with caching."""
|
||||
# Check cache first (significant speedup for repeated tokens)
|
||||
cache_key = (token_string, index % 7)
|
||||
if cache_key in token_id_cache:
|
||||
return token_id_cache[cache_key]
|
||||
|
||||
# Early rejection for obvious non-matches
|
||||
if CUSTOM_TOKEN_PREFIX not in token_string:
|
||||
return None
|
||||
|
||||
# Process token
|
||||
token_string = token_string.strip()
|
||||
last_token_start = token_string.rfind(CUSTOM_TOKEN_PREFIX)
|
||||
|
||||
if last_token_start == -1:
|
||||
return None
|
||||
|
||||
last_token = token_string[last_token_start:]
|
||||
|
||||
if not (last_token.startswith(CUSTOM_TOKEN_PREFIX) and last_token.endswith(">")):
|
||||
return None
|
||||
|
||||
try:
|
||||
number_str = last_token[14:-1]
|
||||
token_id = int(number_str) - 10 - ((index % 7) * 4096)
|
||||
|
||||
# Cache the result if it's valid
|
||||
if len(token_id_cache) < MAX_CACHE_SIZE:
|
||||
token_id_cache[cache_key] = token_id
|
||||
|
||||
return token_id
|
||||
except (ValueError, IndexError):
|
||||
return None
|
||||
# The turn_token_into_id function is now imported from speechpipe.py
|
||||
# This eliminates duplicate code and ensures consistent behavior
|
||||
|
||||
def convert_to_audio(multiframe: List[int], count: int) -> Optional[bytes]:
|
||||
"""Convert token frames to audio with performance monitoring."""
|
||||
|
|
@ -365,13 +331,15 @@ def convert_to_audio(multiframe: List[int], count: int) -> Optional[bytes]:
|
|||
return result
|
||||
|
||||
async def tokens_decoder(token_gen) -> Generator[bytes, None, None]:
|
||||
"""Simplified token decoder without complex ring buffer to ensure reliable output."""
|
||||
"""Simplified token decoder with early first-chunk processing for lower latency."""
|
||||
buffer = []
|
||||
count = 0
|
||||
|
||||
# Use conservative batch parameters to ensure output quality
|
||||
min_frames = 28 # Default for reliability (4 chunks of 7)
|
||||
process_every = 7 # Process every 7 tokens (standard for Orpheus)
|
||||
# Use different thresholds for first chunk vs. subsequent chunks
|
||||
first_chunk_processed = False
|
||||
min_frames_first = 7 # Process after just 7 tokens for first chunk (ultra-low latency)
|
||||
min_frames_subsequent = 28 # Default for reliability after first chunk (4 chunks of 7)
|
||||
process_every = 7 # Process every 7 tokens (standard for Orpheus model)
|
||||
|
||||
start_time = time.time()
|
||||
last_log_time = start_time
|
||||
|
|
@ -393,19 +361,32 @@ async def tokens_decoder(token_gen) -> Generator[bytes, None, None]:
|
|||
print(f"Token processing rate: {token_count/elapsed:.1f} tokens/second")
|
||||
last_log_time = current_time
|
||||
|
||||
# Process in standard batches for Orpheus model
|
||||
if count % process_every == 0 and count >= min_frames:
|
||||
# Use simple slice operation - reliable and correct
|
||||
buffer_to_proc = buffer[-min_frames:]
|
||||
|
||||
# Debug output to help diagnose issues
|
||||
if count % 28 == 0:
|
||||
print(f"Processing buffer with {len(buffer_to_proc)} tokens, total collected: {len(buffer)}")
|
||||
|
||||
# Process the tokens
|
||||
audio_samples = convert_to_audio(buffer_to_proc, count)
|
||||
if audio_samples is not None:
|
||||
yield audio_samples
|
||||
# Different processing paths based on whether first chunk has been processed
|
||||
if not first_chunk_processed:
|
||||
# For first audio output, process as soon as we have enough tokens for one chunk
|
||||
if count >= min_frames_first:
|
||||
buffer_to_proc = buffer[-min_frames_first:]
|
||||
|
||||
# Process the first chunk for immediate audio feedback
|
||||
print(f"Processing first audio chunk with {len(buffer_to_proc)} tokens")
|
||||
audio_samples = convert_to_audio(buffer_to_proc, count)
|
||||
if audio_samples is not None:
|
||||
first_chunk_processed = True # Mark first chunk as processed
|
||||
yield audio_samples
|
||||
else:
|
||||
# For subsequent chunks, use standard processing with larger batch
|
||||
if count % process_every == 0 and count >= min_frames_subsequent:
|
||||
# Use simple slice operation - reliable and correct
|
||||
buffer_to_proc = buffer[-min_frames_subsequent:]
|
||||
|
||||
# Debug output to help diagnose issues
|
||||
if count % 28 == 0:
|
||||
print(f"Processing buffer with {len(buffer_to_proc)} tokens, total collected: {len(buffer)}")
|
||||
|
||||
# Process the tokens
|
||||
audio_samples = convert_to_audio(buffer_to_proc, count)
|
||||
if audio_samples is not None:
|
||||
yield audio_samples
|
||||
|
||||
def tokens_decoder_sync(syn_token_gen, output_file=None):
|
||||
"""Optimized synchronous wrapper with parallel processing and efficient file I/O."""
|
||||
|
|
|
|||
|
|
@ -5,13 +5,25 @@ import asyncio
|
|||
import threading
|
||||
import queue
|
||||
import time
|
||||
import os
|
||||
import sys
|
||||
|
||||
# Helper to detect if running in Uvicorn's reloader (same as in inference.py)
|
||||
def is_reloader_process():
|
||||
"""Check if the current process is a uvicorn reloader"""
|
||||
return (sys.argv[0].endswith('_continuation.py') or
|
||||
os.environ.get('UVICORN_STARTED') == 'true')
|
||||
|
||||
# Set a flag to avoid repeat messages
|
||||
IS_RELOADER = is_reloader_process()
|
||||
|
||||
# Try to enable torch.compile if PyTorch 2.0+ is available
|
||||
TORCH_COMPILE_AVAILABLE = False
|
||||
try:
|
||||
if hasattr(torch, 'compile'):
|
||||
TORCH_COMPILE_AVAILABLE = True
|
||||
print("PyTorch 2.0+ detected, torch.compile is available")
|
||||
if not IS_RELOADER:
|
||||
print("PyTorch 2.0+ detected, torch.compile is available")
|
||||
except:
|
||||
pass
|
||||
|
||||
|
|
@ -20,7 +32,8 @@ CUDA_GRAPHS_AVAILABLE = False
|
|||
try:
|
||||
if torch.cuda.is_available() and hasattr(torch.cuda, 'make_graphed_callables'):
|
||||
CUDA_GRAPHS_AVAILABLE = True
|
||||
print("CUDA graphs support is available")
|
||||
if not IS_RELOADER:
|
||||
print("CUDA graphs support is available")
|
||||
except:
|
||||
pass
|
||||
|
||||
|
|
@ -28,18 +41,21 @@ model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval()
|
|||
|
||||
# Check if CUDA is available and set device accordingly
|
||||
snac_device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
|
||||
print(f"Using device: {snac_device}")
|
||||
if not IS_RELOADER:
|
||||
print(f"Using device: {snac_device}")
|
||||
model = model.to(snac_device)
|
||||
|
||||
# Disable torch.compile as it requires Triton which isn't installed
|
||||
# We'll use regular PyTorch optimization techniques instead
|
||||
print("Using standard PyTorch optimizations (torch.compile disabled)")
|
||||
if not IS_RELOADER:
|
||||
print("Using standard PyTorch optimizations (torch.compile disabled)")
|
||||
|
||||
# Prepare CUDA streams for parallel processing if available
|
||||
cuda_stream = None
|
||||
if snac_device == "cuda":
|
||||
cuda_stream = torch.cuda.Stream()
|
||||
print("Using CUDA stream for parallel processing")
|
||||
if not IS_RELOADER:
|
||||
print("Using CUDA stream for parallel processing")
|
||||
|
||||
|
||||
def convert_to_audio(multiframe, count):
|
||||
|
|
@ -117,41 +133,71 @@ def convert_to_audio(multiframe, count):
|
|||
|
||||
return audio_bytes
|
||||
|
||||
# Define the custom token prefix
|
||||
CUSTOM_TOKEN_PREFIX = "<custom_token_"
|
||||
|
||||
# Use a single global cache for token processing
|
||||
token_id_cache = {}
|
||||
MAX_CACHE_SIZE = 10000 # Increased cache size for better performance
|
||||
|
||||
def turn_token_into_id(token_string, index):
|
||||
"""Optimized token-to-id conversion with early returns and minimal string operations"""
|
||||
token_string = token_string.strip()
|
||||
"""
|
||||
Optimized token-to-ID conversion with caching.
|
||||
This is the definitive implementation used by both inference.py and speechpipe.py.
|
||||
|
||||
# Early return for obvious mismatches
|
||||
if "<custom_token_" not in token_string:
|
||||
Args:
|
||||
token_string: The token string to convert
|
||||
index: Position index used for token offset calculation
|
||||
|
||||
Returns:
|
||||
int: Token ID if valid, None otherwise
|
||||
"""
|
||||
# Check cache first (significant speedup for repeated tokens)
|
||||
cache_key = (token_string, index % 7)
|
||||
if cache_key in token_id_cache:
|
||||
return token_id_cache[cache_key]
|
||||
|
||||
# Early rejection for obvious non-matches
|
||||
if CUSTOM_TOKEN_PREFIX not in token_string:
|
||||
return None
|
||||
|
||||
# Process token
|
||||
token_string = token_string.strip()
|
||||
last_token_start = token_string.rfind(CUSTOM_TOKEN_PREFIX)
|
||||
|
||||
# Find the last token in the string
|
||||
last_token_start = token_string.rfind("<custom_token_")
|
||||
if last_token_start == -1:
|
||||
return None
|
||||
|
||||
# Check if the token ends properly
|
||||
if not token_string.endswith(">"):
|
||||
last_token = token_string[last_token_start:]
|
||||
|
||||
if not (last_token.startswith(CUSTOM_TOKEN_PREFIX) and last_token.endswith(">")):
|
||||
return None
|
||||
|
||||
try:
|
||||
# Extract and convert the number directly
|
||||
number_str = token_string[last_token_start+14:-1]
|
||||
return int(number_str) - 10 - ((index % 7) * 4096)
|
||||
number_str = last_token[14:-1]
|
||||
token_id = int(number_str) - 10 - ((index % 7) * 4096)
|
||||
|
||||
# Cache the result if it's valid
|
||||
if len(token_id_cache) < MAX_CACHE_SIZE:
|
||||
token_id_cache[cache_key] = token_id
|
||||
|
||||
return token_id
|
||||
except (ValueError, IndexError):
|
||||
return None
|
||||
# Cache for frequently processed tokens to avoid redundant computation
|
||||
token_cache = {}
|
||||
MAX_CACHE_SIZE = 1000 # Limit cache size to prevent memory bloat
|
||||
|
||||
async def tokens_decoder(token_gen):
|
||||
"""Optimized token decoder with reliable end-of-buffer handling for complete audio generation"""
|
||||
"""Optimized token decoder with early first-chunk processing for lower latency"""
|
||||
buffer = []
|
||||
count = 0
|
||||
# Use a smaller minimum frame requirement to allow more flexible processing
|
||||
min_frames_required = 28 # Lower requirement (4 chunks of 7 tokens)
|
||||
ideal_frames = 49 # Ideal standard frame size (7×7 window)
|
||||
process_every_n = 7 # Process every 7 tokens (standard for Orpheus model)
|
||||
|
||||
# Track if first chunk has been processed
|
||||
first_chunk_processed = False
|
||||
|
||||
# Use different thresholds for first chunk vs. subsequent chunks
|
||||
min_frames_first = 7 # Just one chunk (7 tokens) for first audio - ultra-low latency
|
||||
min_frames_subsequent = 28 # Standard minimum (4 chunks of 7 tokens) after first audio
|
||||
ideal_frames = 49 # Ideal standard frame size (7×7 window) - unchanged
|
||||
process_every_n = 7 # Process every 7 tokens (standard for Orpheus model) - unchanged
|
||||
|
||||
start_time = time.time()
|
||||
token_count = 0
|
||||
|
|
@ -160,15 +206,8 @@ async def tokens_decoder(token_gen):
|
|||
async for token_sim in token_gen:
|
||||
token_count += 1
|
||||
|
||||
# Check cache first to avoid redundant computation
|
||||
cache_key = (token_sim, count % 7)
|
||||
if cache_key in token_cache:
|
||||
token = token_cache[cache_key]
|
||||
else:
|
||||
token = turn_token_into_id(token_sim, count)
|
||||
# Add to cache if valid token
|
||||
if token is not None and len(token_cache) < MAX_CACHE_SIZE:
|
||||
token_cache[cache_key] = token
|
||||
# Use the unified turn_token_into_id which already handles caching
|
||||
token = turn_token_into_id(token_sim, count)
|
||||
|
||||
if token is not None and token > 0:
|
||||
buffer.append(token)
|
||||
|
|
@ -185,26 +224,37 @@ async def tokens_decoder(token_gen):
|
|||
last_log_time = current_time
|
||||
token_count = 0
|
||||
|
||||
# Process standard batches when we have enough tokens
|
||||
if count % process_every_n == 0:
|
||||
# Best case: we have enough for the ideal frame size
|
||||
if len(buffer) >= ideal_frames:
|
||||
buffer_to_proc = buffer[-ideal_frames:]
|
||||
# Fallback: we have enough for the minimum requirement
|
||||
elif len(buffer) >= min_frames_required:
|
||||
buffer_to_proc = buffer[-min_frames_required:]
|
||||
# For the first few frames, we may not have enough yet
|
||||
else:
|
||||
continue
|
||||
|
||||
# Debug output to help diagnose issues
|
||||
if count % 28 == 0:
|
||||
print(f"Processing buffer with {len(buffer_to_proc)} tokens, total collected: {len(buffer)}")
|
||||
|
||||
# Process the tokens
|
||||
audio_samples = convert_to_audio(buffer_to_proc, count)
|
||||
if audio_samples is not None:
|
||||
yield audio_samples
|
||||
# Different processing logic based on whether first chunk has been processed
|
||||
if not first_chunk_processed:
|
||||
# Process first chunk as soon as possible for minimal latency
|
||||
if count >= min_frames_first:
|
||||
buffer_to_proc = buffer[-min_frames_first:]
|
||||
|
||||
# Process the first chunk of audio for immediate feedback
|
||||
print(f"Processing first audio chunk with {len(buffer_to_proc)} tokens for low latency")
|
||||
audio_samples = convert_to_audio(buffer_to_proc, count)
|
||||
if audio_samples is not None:
|
||||
first_chunk_processed = True # Mark first chunk as processed
|
||||
yield audio_samples
|
||||
else:
|
||||
# For subsequent chunks, use original processing with proper batching
|
||||
if count % process_every_n == 0:
|
||||
# Use same prioritization logic as before
|
||||
if len(buffer) >= ideal_frames:
|
||||
buffer_to_proc = buffer[-ideal_frames:]
|
||||
elif len(buffer) >= min_frames_subsequent:
|
||||
buffer_to_proc = buffer[-min_frames_subsequent:]
|
||||
else:
|
||||
continue
|
||||
|
||||
# Debug output to help diagnose issues
|
||||
if count % 28 == 0:
|
||||
print(f"Processing buffer with {len(buffer_to_proc)} tokens, total collected: {len(buffer)}")
|
||||
|
||||
# Process the tokens
|
||||
audio_samples = convert_to_audio(buffer_to_proc, count)
|
||||
if audio_samples is not None:
|
||||
yield audio_samples
|
||||
|
||||
# CRITICAL: End-of-generation handling - process all remaining frames
|
||||
# Process remaining complete frames (ideal size)
|
||||
|
|
@ -215,8 +265,8 @@ async def tokens_decoder(token_gen):
|
|||
yield audio_samples
|
||||
|
||||
# Process any additional complete frames (minimum size)
|
||||
elif len(buffer) >= min_frames_required:
|
||||
buffer_to_proc = buffer[-min_frames_required:]
|
||||
elif len(buffer) >= min_frames_subsequent:
|
||||
buffer_to_proc = buffer[-min_frames_subsequent:]
|
||||
audio_samples = convert_to_audio(buffer_to_proc, count)
|
||||
if audio_samples is not None:
|
||||
yield audio_samples
|
||||
|
|
@ -227,7 +277,7 @@ async def tokens_decoder(token_gen):
|
|||
# Pad to minimum frame requirement with copies of the final token
|
||||
# This is more continuous than using unrelated tokens from the beginning
|
||||
last_token = buffer[-1]
|
||||
padding_needed = min_frames_required - len(buffer)
|
||||
padding_needed = min_frames_subsequent - len(buffer)
|
||||
|
||||
# Create a padding array of copies of the last token
|
||||
# This maintains continuity much better than circular buffering
|
||||
|
|
|
|||
Loading…
Reference in a new issue