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
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@ -140,10 +140,12 @@ NUM_WORKERS = 4 if HIGH_END_GPU else 2
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AVAILABLE_VOICES = ["tara", "leah", "jess", "leo", "dan", "mia", "zac", "zoe"]
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DEFAULT_VOICE = "tara" # Best voice according to documentation
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# Import the unified token handling from speechpipe
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from .speechpipe import turn_token_into_id, CUSTOM_TOKEN_PREFIX
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# Special token IDs for Orpheus model
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START_TOKEN_ID = 128259
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END_TOKEN_IDS = [128009, 128260, 128261, 128257]
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CUSTOM_TOKEN_PREFIX = "<custom_token_"
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# Performance monitoring
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class PerformanceMonitor:
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@ -218,7 +220,7 @@ def generate_tokens_from_api(prompt: str, voice: str = DEFAULT_VOICE, temperatur
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# Create the request payload
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payload = {
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"model": "orpheus-3b-0.1-ft-q4_k_m", # Model name can be anything, endpoint will use loaded model
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"model": "Orpheus-3b-FT-Q8_0.gguf", # Model name can be anything, endpoint will use loaded model
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"prompt": formatted_prompt,
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"max_tokens": max_tokens,
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"temperature": temperature,
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@ -313,44 +315,8 @@ def generate_tokens_from_api(prompt: str, voice: str = DEFAULT_VOICE, temperatur
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print("Max retries reached. Token generation failed.")
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return
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# Token ID cache to avoid repeated processing
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token_id_cache = {}
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MAX_CACHE_SIZE = 10000
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def turn_token_into_id(token_string: str, index: int) -> Optional[int]:
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"""Optimized token-to-ID conversion with caching."""
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# Check cache first (significant speedup for repeated tokens)
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cache_key = (token_string, index % 7)
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if cache_key in token_id_cache:
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return token_id_cache[cache_key]
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# Early rejection for obvious non-matches
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if CUSTOM_TOKEN_PREFIX not in token_string:
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return None
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# Process token
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token_string = token_string.strip()
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last_token_start = token_string.rfind(CUSTOM_TOKEN_PREFIX)
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if last_token_start == -1:
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return None
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last_token = token_string[last_token_start:]
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if not (last_token.startswith(CUSTOM_TOKEN_PREFIX) and last_token.endswith(">")):
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return None
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try:
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number_str = last_token[14:-1]
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token_id = int(number_str) - 10 - ((index % 7) * 4096)
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# Cache the result if it's valid
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if len(token_id_cache) < MAX_CACHE_SIZE:
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token_id_cache[cache_key] = token_id
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return token_id
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except (ValueError, IndexError):
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return None
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# The turn_token_into_id function is now imported from speechpipe.py
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# This eliminates duplicate code and ensures consistent behavior
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def convert_to_audio(multiframe: List[int], count: int) -> Optional[bytes]:
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"""Convert token frames to audio with performance monitoring."""
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@ -365,13 +331,15 @@ def convert_to_audio(multiframe: List[int], count: int) -> Optional[bytes]:
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return result
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async def tokens_decoder(token_gen) -> Generator[bytes, None, None]:
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"""Simplified token decoder without complex ring buffer to ensure reliable output."""
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"""Simplified token decoder with early first-chunk processing for lower latency."""
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buffer = []
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count = 0
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# Use conservative batch parameters to ensure output quality
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min_frames = 28 # Default for reliability (4 chunks of 7)
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process_every = 7 # Process every 7 tokens (standard for Orpheus)
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# Use different thresholds for first chunk vs. subsequent chunks
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first_chunk_processed = False
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min_frames_first = 7 # Process after just 7 tokens for first chunk (ultra-low latency)
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min_frames_subsequent = 28 # Default for reliability after first chunk (4 chunks of 7)
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process_every = 7 # Process every 7 tokens (standard for Orpheus model)
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start_time = time.time()
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last_log_time = start_time
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@ -393,10 +361,23 @@ async def tokens_decoder(token_gen) -> Generator[bytes, None, None]:
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print(f"Token processing rate: {token_count/elapsed:.1f} tokens/second")
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last_log_time = current_time
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# Process in standard batches for Orpheus model
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if count % process_every == 0 and count >= min_frames:
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# Different processing paths based on whether first chunk has been processed
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if not first_chunk_processed:
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# For first audio output, process as soon as we have enough tokens for one chunk
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if count >= min_frames_first:
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buffer_to_proc = buffer[-min_frames_first:]
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# Process the first chunk for immediate audio feedback
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print(f"Processing first audio chunk with {len(buffer_to_proc)} tokens")
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audio_samples = convert_to_audio(buffer_to_proc, count)
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if audio_samples is not None:
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first_chunk_processed = True # Mark first chunk as processed
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yield audio_samples
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else:
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# For subsequent chunks, use standard processing with larger batch
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if count % process_every == 0 and count >= min_frames_subsequent:
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# Use simple slice operation - reliable and correct
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buffer_to_proc = buffer[-min_frames:]
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buffer_to_proc = buffer[-min_frames_subsequent:]
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# Debug output to help diagnose issues
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if count % 28 == 0:
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@ -5,12 +5,24 @@ import asyncio
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import threading
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import queue
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import time
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import os
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import sys
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# Helper to detect if running in Uvicorn's reloader (same as in inference.py)
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def is_reloader_process():
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"""Check if the current process is a uvicorn reloader"""
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return (sys.argv[0].endswith('_continuation.py') or
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os.environ.get('UVICORN_STARTED') == 'true')
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# Set a flag to avoid repeat messages
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IS_RELOADER = is_reloader_process()
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# Try to enable torch.compile if PyTorch 2.0+ is available
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TORCH_COMPILE_AVAILABLE = False
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try:
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if hasattr(torch, 'compile'):
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TORCH_COMPILE_AVAILABLE = True
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if not IS_RELOADER:
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print("PyTorch 2.0+ detected, torch.compile is available")
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except:
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pass
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@ -20,6 +32,7 @@ CUDA_GRAPHS_AVAILABLE = False
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try:
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if torch.cuda.is_available() and hasattr(torch.cuda, 'make_graphed_callables'):
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CUDA_GRAPHS_AVAILABLE = True
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if not IS_RELOADER:
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print("CUDA graphs support is available")
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except:
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pass
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@ -28,17 +41,20 @@ model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval()
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# Check if CUDA is available and set device accordingly
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snac_device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
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print(f"Using device: {snac_device}")
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if not IS_RELOADER:
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print(f"Using device: {snac_device}")
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model = model.to(snac_device)
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# Disable torch.compile as it requires Triton which isn't installed
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# We'll use regular PyTorch optimization techniques instead
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print("Using standard PyTorch optimizations (torch.compile disabled)")
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if not IS_RELOADER:
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print("Using standard PyTorch optimizations (torch.compile disabled)")
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# Prepare CUDA streams for parallel processing if available
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cuda_stream = None
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if snac_device == "cuda":
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cuda_stream = torch.cuda.Stream()
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if not IS_RELOADER:
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print("Using CUDA stream for parallel processing")
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@ -117,41 +133,71 @@ def convert_to_audio(multiframe, count):
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return audio_bytes
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def turn_token_into_id(token_string, index):
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"""Optimized token-to-id conversion with early returns and minimal string operations"""
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token_string = token_string.strip()
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# Define the custom token prefix
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CUSTOM_TOKEN_PREFIX = "<custom_token_"
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# Early return for obvious mismatches
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if "<custom_token_" not in token_string:
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# Use a single global cache for token processing
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token_id_cache = {}
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MAX_CACHE_SIZE = 10000 # Increased cache size for better performance
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def turn_token_into_id(token_string, index):
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"""
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Optimized token-to-ID conversion with caching.
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This is the definitive implementation used by both inference.py and speechpipe.py.
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Args:
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token_string: The token string to convert
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index: Position index used for token offset calculation
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Returns:
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int: Token ID if valid, None otherwise
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"""
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# Check cache first (significant speedup for repeated tokens)
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cache_key = (token_string, index % 7)
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if cache_key in token_id_cache:
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return token_id_cache[cache_key]
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# Early rejection for obvious non-matches
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if CUSTOM_TOKEN_PREFIX not in token_string:
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return None
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# Find the last token in the string
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last_token_start = token_string.rfind("<custom_token_")
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# Process token
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token_string = token_string.strip()
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last_token_start = token_string.rfind(CUSTOM_TOKEN_PREFIX)
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if last_token_start == -1:
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return None
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# Check if the token ends properly
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if not token_string.endswith(">"):
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last_token = token_string[last_token_start:]
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if not (last_token.startswith(CUSTOM_TOKEN_PREFIX) and last_token.endswith(">")):
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return None
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try:
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# Extract and convert the number directly
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number_str = token_string[last_token_start+14:-1]
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return int(number_str) - 10 - ((index % 7) * 4096)
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number_str = last_token[14:-1]
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token_id = int(number_str) - 10 - ((index % 7) * 4096)
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# Cache the result if it's valid
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if len(token_id_cache) < MAX_CACHE_SIZE:
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token_id_cache[cache_key] = token_id
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return token_id
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except (ValueError, IndexError):
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return None
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# Cache for frequently processed tokens to avoid redundant computation
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token_cache = {}
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MAX_CACHE_SIZE = 1000 # Limit cache size to prevent memory bloat
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async def tokens_decoder(token_gen):
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"""Optimized token decoder with reliable end-of-buffer handling for complete audio generation"""
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"""Optimized token decoder with early first-chunk processing for lower latency"""
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buffer = []
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count = 0
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# Use a smaller minimum frame requirement to allow more flexible processing
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min_frames_required = 28 # Lower requirement (4 chunks of 7 tokens)
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ideal_frames = 49 # Ideal standard frame size (7×7 window)
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process_every_n = 7 # Process every 7 tokens (standard for Orpheus model)
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# Track if first chunk has been processed
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first_chunk_processed = False
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# Use different thresholds for first chunk vs. subsequent chunks
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min_frames_first = 7 # Just one chunk (7 tokens) for first audio - ultra-low latency
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min_frames_subsequent = 28 # Standard minimum (4 chunks of 7 tokens) after first audio
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ideal_frames = 49 # Ideal standard frame size (7×7 window) - unchanged
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process_every_n = 7 # Process every 7 tokens (standard for Orpheus model) - unchanged
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start_time = time.time()
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token_count = 0
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@ -160,15 +206,8 @@ async def tokens_decoder(token_gen):
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async for token_sim in token_gen:
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token_count += 1
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# Check cache first to avoid redundant computation
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cache_key = (token_sim, count % 7)
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if cache_key in token_cache:
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token = token_cache[cache_key]
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else:
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# Use the unified turn_token_into_id which already handles caching
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token = turn_token_into_id(token_sim, count)
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# Add to cache if valid token
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if token is not None and len(token_cache) < MAX_CACHE_SIZE:
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token_cache[cache_key] = token
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if token is not None and token > 0:
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buffer.append(token)
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@ -185,15 +224,26 @@ async def tokens_decoder(token_gen):
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last_log_time = current_time
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token_count = 0
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# Process standard batches when we have enough tokens
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# Different processing logic based on whether first chunk has been processed
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if not first_chunk_processed:
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# Process first chunk as soon as possible for minimal latency
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if count >= min_frames_first:
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buffer_to_proc = buffer[-min_frames_first:]
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# Process the first chunk of audio for immediate feedback
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print(f"Processing first audio chunk with {len(buffer_to_proc)} tokens for low latency")
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audio_samples = convert_to_audio(buffer_to_proc, count)
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if audio_samples is not None:
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first_chunk_processed = True # Mark first chunk as processed
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yield audio_samples
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else:
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# For subsequent chunks, use original processing with proper batching
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if count % process_every_n == 0:
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# Best case: we have enough for the ideal frame size
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# Use same prioritization logic as before
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if len(buffer) >= ideal_frames:
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buffer_to_proc = buffer[-ideal_frames:]
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# Fallback: we have enough for the minimum requirement
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elif len(buffer) >= min_frames_required:
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buffer_to_proc = buffer[-min_frames_required:]
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# For the first few frames, we may not have enough yet
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elif len(buffer) >= min_frames_subsequent:
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buffer_to_proc = buffer[-min_frames_subsequent:]
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else:
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continue
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@ -215,8 +265,8 @@ async def tokens_decoder(token_gen):
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yield audio_samples
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# Process any additional complete frames (minimum size)
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elif len(buffer) >= min_frames_required:
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buffer_to_proc = buffer[-min_frames_required:]
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elif len(buffer) >= min_frames_subsequent:
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buffer_to_proc = buffer[-min_frames_subsequent:]
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audio_samples = convert_to_audio(buffer_to_proc, count)
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if audio_samples is not None:
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yield audio_samples
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@ -227,7 +277,7 @@ async def tokens_decoder(token_gen):
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# Pad to minimum frame requirement with copies of the final token
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# This is more continuous than using unrelated tokens from the beginning
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last_token = buffer[-1]
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padding_needed = min_frames_required - len(buffer)
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padding_needed = min_frames_subsequent - len(buffer)
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# Create a padding array of copies of the last token
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# This maintains continuity much better than circular buffering
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