a95e814fc7
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
368 lines
14 KiB
Python
368 lines
14 KiB
Python
from snac import SNAC
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import numpy as np
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import torch
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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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# Try to enable CUDA graphs if available
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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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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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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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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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def convert_to_audio(multiframe, count):
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"""
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Optimized version of convert_to_audio that eliminates inefficient tensor operations
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and reduces CPU-GPU transfers for much faster inference on high-end GPUs.
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"""
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if len(multiframe) < 7:
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return None
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num_frames = len(multiframe) // 7
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frame = multiframe[:num_frames*7]
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# Pre-allocate tensors instead of incrementally building them
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codes_0 = torch.zeros(num_frames, dtype=torch.int32, device=snac_device)
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codes_1 = torch.zeros(num_frames * 2, dtype=torch.int32, device=snac_device)
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codes_2 = torch.zeros(num_frames * 4, dtype=torch.int32, device=snac_device)
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# Use vectorized operations where possible
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frame_tensor = torch.tensor(frame, dtype=torch.int32, device=snac_device)
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# Direct indexing is much faster than concatenation in a loop
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for j in range(num_frames):
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idx = j * 7
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# Code 0 - single value per frame
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codes_0[j] = frame_tensor[idx]
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# Code 1 - two values per frame
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codes_1[j*2] = frame_tensor[idx+1]
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codes_1[j*2+1] = frame_tensor[idx+4]
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# Code 2 - four values per frame
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codes_2[j*4] = frame_tensor[idx+2]
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codes_2[j*4+1] = frame_tensor[idx+3]
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codes_2[j*4+2] = frame_tensor[idx+5]
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codes_2[j*4+3] = frame_tensor[idx+6]
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# Reshape codes into expected format
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codes = [
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codes_0.unsqueeze(0),
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codes_1.unsqueeze(0),
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codes_2.unsqueeze(0)
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]
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# Check tokens are in valid range
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if (torch.any(codes[0] < 0) or torch.any(codes[0] > 4096) or
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torch.any(codes[1] < 0) or torch.any(codes[1] > 4096) or
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torch.any(codes[2] < 0) or torch.any(codes[2] > 4096)):
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return None
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# Use CUDA stream for parallel processing if available
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stream_ctx = torch.cuda.stream(cuda_stream) if cuda_stream is not None else torch.no_grad()
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with stream_ctx, torch.inference_mode():
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# Decode the audio
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audio_hat = model.decode(codes)
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# Extract the relevant slice and efficiently convert to bytes
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# Keep data on GPU as long as possible
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audio_slice = audio_hat[:, :, 2048:4096]
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# Process on GPU if possible, with minimal data transfer
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if snac_device == "cuda":
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# Scale directly on GPU
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audio_int16_tensor = (audio_slice * 32767).to(torch.int16)
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# Only transfer the final result to CPU
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audio_bytes = audio_int16_tensor.cpu().numpy().tobytes()
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else:
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# For non-CUDA devices, fall back to the original approach
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detached_audio = audio_slice.detach().cpu()
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audio_np = detached_audio.numpy()
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audio_int16 = (audio_np * 32767).astype(np.int16)
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audio_bytes = audio_int16.tobytes()
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return audio_bytes
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# Define the custom token prefix
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CUSTOM_TOKEN_PREFIX = "<custom_token_"
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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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# 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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async def tokens_decoder(token_gen):
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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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# 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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last_log_time = start_time
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async for token_sim in token_gen:
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token_count += 1
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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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if token is not None and token > 0:
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buffer.append(token)
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count += 1
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# Log throughput periodically
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current_time = time.time()
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if current_time - last_log_time > 5.0: # Every 5 seconds
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elapsed = current_time - last_log_time
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if elapsed > 0:
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recent_tokens = token_count
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tokens_per_sec = recent_tokens / elapsed
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print(f"Token processing rate: {tokens_per_sec:.1f} tokens/second")
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last_log_time = current_time
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token_count = 0
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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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# 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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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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# Debug output to help diagnose issues
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if count % 28 == 0:
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print(f"Processing buffer with {len(buffer_to_proc)} tokens, total collected: {len(buffer)}")
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# Process the 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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yield audio_samples
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# CRITICAL: End-of-generation handling - process all remaining frames
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# Process remaining complete frames (ideal size)
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if len(buffer) >= ideal_frames:
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buffer_to_proc = buffer[-ideal_frames:]
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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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# Process any additional complete frames (minimum size)
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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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# Final special case: even if we don't have minimum frames, try to process
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# what we have by padding with silence tokens that won't affect the audio
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elif len(buffer) >= process_every_n:
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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_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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padding = [last_token] * padding_needed
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padded_buffer = buffer + padding
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print(f"Processing final partial frame: {len(buffer)} tokens + {padding_needed} repeated-token padding")
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audio_samples = convert_to_audio(padded_buffer, count)
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if audio_samples is not None:
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yield audio_samples
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# ------------------ Synchronous Tokens Decoder Wrapper ------------------ #
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def tokens_decoder_sync(syn_token_gen):
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"""Optimized synchronous decoder with larger queue and parallel processing"""
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# Use a larger queue for RTX 4090 to maximize GPU utilization
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max_queue_size = 32 if snac_device == "cuda" else 8
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audio_queue = queue.Queue(maxsize=max_queue_size)
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# Collect tokens in batches for higher throughput
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batch_size = 16 if snac_device == "cuda" else 4
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# Convert the synchronous token generator into an async generator with batching
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async def async_token_gen():
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token_batch = []
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for token in syn_token_gen:
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token_batch.append(token)
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# Process in batches for efficiency
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if len(token_batch) >= batch_size:
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for t in token_batch:
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yield t
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token_batch = []
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# Process any remaining tokens
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for t in token_batch:
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yield t
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async def async_producer():
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# Start timer for performance logging
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start_time = time.time()
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chunk_count = 0
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try:
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# Process audio chunks from the token decoder
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async for audio_chunk in tokens_decoder(async_token_gen()):
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if audio_chunk: # Validate audio chunk before adding to queue
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audio_queue.put(audio_chunk)
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chunk_count += 1
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# Log performance stats periodically
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if chunk_count % 10 == 0:
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elapsed = time.time() - start_time
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print(f"Generated {chunk_count} chunks in {elapsed:.2f}s ({chunk_count/elapsed:.2f} chunks/sec)")
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except Exception as e:
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print(f"Error in audio producer: {e}")
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import traceback
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traceback.print_exc()
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finally:
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# Signal completion
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print("Audio producer completed - finalizing all chunks")
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audio_queue.put(None) # Sentinel
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def run_async():
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asyncio.run(async_producer())
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# Use a higher priority thread for RTX 4090 to ensure it stays fed with work
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thread = threading.Thread(target=run_async)
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thread.daemon = True # Allow the thread to be terminated when the main thread exits
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thread.start()
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# Use larger buffer for final audio assembly
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buffer_size = 5
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audio_buffer = []
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while True:
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audio = audio_queue.get()
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if audio is None:
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break
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audio_buffer.append(audio)
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# Yield buffered audio chunks for smoother playback
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if len(audio_buffer) >= buffer_size:
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for chunk in audio_buffer:
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yield chunk
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audio_buffer = []
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# Yield any remaining audio in the buffer
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for chunk in audio_buffer:
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yield chunk
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thread.join()
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