add VibeVoice-Realtime
This commit is contained in:
YaoyaoChang
@@ -0,0 +1,314 @@
|
||||
import argparse
|
||||
import os
|
||||
import re
|
||||
import traceback
|
||||
from typing import List, Tuple, Union, Dict, Any
|
||||
import time
|
||||
import torch
|
||||
import copy
|
||||
|
||||
from vibevoice.modular.modeling_vibevoice_streaming_inference import VibeVoiceStreamingForConditionalGenerationInference
|
||||
from vibevoice.processor.vibevoice_streaming_processor import VibeVoiceStreamingProcessor
|
||||
from transformers.utils import logging
|
||||
|
||||
logging.set_verbosity_info()
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class VoiceMapper:
|
||||
"""Maps speaker names to voice file paths"""
|
||||
|
||||
def __init__(self):
|
||||
self.setup_voice_presets()
|
||||
|
||||
# change name according to our preset voice file
|
||||
new_dict = {}
|
||||
for name, path in self.voice_presets.items():
|
||||
|
||||
if '_' in name:
|
||||
name = name.split('_')[0]
|
||||
|
||||
if '-' in name:
|
||||
name = name.split('-')[-1]
|
||||
|
||||
new_dict[name] = path
|
||||
self.voice_presets.update(new_dict)
|
||||
# print(list(self.voice_presets.keys()))
|
||||
|
||||
def setup_voice_presets(self):
|
||||
"""Setup voice presets by scanning the voices directory."""
|
||||
voices_dir = os.path.join(os.path.dirname(__file__), "voices/streaming_model")
|
||||
|
||||
# Check if voices directory exists
|
||||
if not os.path.exists(voices_dir):
|
||||
print(f"Warning: Voices directory not found at {voices_dir}")
|
||||
self.voice_presets = {}
|
||||
self.available_voices = {}
|
||||
return
|
||||
|
||||
# Scan for all VOICE files in the voices directory
|
||||
self.voice_presets = {}
|
||||
|
||||
# Get all .pt files in the voices directory
|
||||
pt_files = [f for f in os.listdir(voices_dir)
|
||||
if f.lower().endswith('.pt') and os.path.isfile(os.path.join(voices_dir, f))]
|
||||
|
||||
# Create dictionary with filename (without extension) as key
|
||||
for pt_file in pt_files:
|
||||
# Remove .pt extension to get the name
|
||||
name = os.path.splitext(pt_file)[0]
|
||||
# Create full path
|
||||
full_path = os.path.join(voices_dir, pt_file)
|
||||
self.voice_presets[name] = full_path
|
||||
|
||||
# Sort the voice presets alphabetically by name for better UI
|
||||
self.voice_presets = dict(sorted(self.voice_presets.items()))
|
||||
|
||||
# Filter out voices that don't exist (this is now redundant but kept for safety)
|
||||
self.available_voices = {
|
||||
name: path for name, path in self.voice_presets.items()
|
||||
if os.path.exists(path)
|
||||
}
|
||||
|
||||
print(f"Found {len(self.available_voices)} voice files in {voices_dir}")
|
||||
print(f"Available voices: {', '.join(self.available_voices.keys())}")
|
||||
|
||||
def get_voice_path(self, speaker_name: str) -> str:
|
||||
"""Get voice file path for a given speaker name"""
|
||||
# First try exact match
|
||||
if speaker_name in self.voice_presets:
|
||||
return self.voice_presets[speaker_name]
|
||||
|
||||
# Try partial matching (case insensitive)
|
||||
speaker_lower = speaker_name.lower()
|
||||
for preset_name, path in self.voice_presets.items():
|
||||
if preset_name.lower() in speaker_lower or speaker_lower in preset_name.lower():
|
||||
return path
|
||||
|
||||
# Default to first voice if no match found
|
||||
default_voice = list(self.voice_presets.values())[0]
|
||||
print(f"Warning: No voice preset found for '{speaker_name}', using default voice: {default_voice}")
|
||||
return default_voice
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser(description="VibeVoiceStreaming Processor TXT Input Test")
|
||||
parser.add_argument(
|
||||
"--model_path",
|
||||
type=str,
|
||||
default="microsoft/VibeVoice-Realtime-0.5B",
|
||||
help="Path to the HuggingFace model directory",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--txt_path",
|
||||
type=str,
|
||||
default="demo/text_examples/1p_vibevoice.txt",
|
||||
help="Path to the txt file containing the script",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--speaker_name",
|
||||
type=str,
|
||||
default="Wayne",
|
||||
help="Single speaker name (e.g., --speaker_name Wayne)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output_dir",
|
||||
type=str,
|
||||
default="./outputs",
|
||||
help="Directory to save output audio files",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default=("cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")),
|
||||
help="Device for inference: cuda | mps | cpu",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--cfg_scale",
|
||||
type=float,
|
||||
default=1.5,
|
||||
help="CFG (Classifier-Free Guidance) scale for generation (default: 1.5)",
|
||||
)
|
||||
|
||||
return parser.parse_args()
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
|
||||
# Normalize potential 'mpx' typo to 'mps'
|
||||
if args.device.lower() == "mpx":
|
||||
print("Note: device 'mpx' detected, treating it as 'mps'.")
|
||||
args.device = "mps"
|
||||
|
||||
# Validate mps availability if requested
|
||||
if args.device == "mps" and not torch.backends.mps.is_available():
|
||||
print("Warning: MPS not available. Falling back to CPU.")
|
||||
args.device = "cpu"
|
||||
|
||||
print(f"Using device: {args.device}")
|
||||
|
||||
# Initialize voice mapper
|
||||
voice_mapper = VoiceMapper()
|
||||
|
||||
# Check if txt file exists
|
||||
if not os.path.exists(args.txt_path):
|
||||
print(f"Error: txt file not found: {args.txt_path}")
|
||||
return
|
||||
|
||||
# Read and parse txt file
|
||||
print(f"Reading script from: {args.txt_path}")
|
||||
with open(args.txt_path, 'r', encoding='utf-8') as f:
|
||||
scripts = f.read().strip()
|
||||
|
||||
if not scripts:
|
||||
print("Error: No valid scripts found in the txt file")
|
||||
return
|
||||
|
||||
full_script = scripts.replace("’", "'").replace('“', '"').replace('”', '"')
|
||||
|
||||
print(f"Loading processor & model from {args.model_path}")
|
||||
processor = VibeVoiceStreamingProcessor.from_pretrained(args.model_path)
|
||||
|
||||
# Decide dtype & attention implementation
|
||||
if args.device == "mps":
|
||||
load_dtype = torch.float32 # MPS requires float32
|
||||
attn_impl_primary = "sdpa" # flash_attention_2 not supported on MPS
|
||||
elif args.device == "cuda":
|
||||
load_dtype = torch.bfloat16
|
||||
attn_impl_primary = "flash_attention_2"
|
||||
else: # cpu
|
||||
load_dtype = torch.float32
|
||||
attn_impl_primary = "sdpa"
|
||||
print(f"Using device: {args.device}, torch_dtype: {load_dtype}, attn_implementation: {attn_impl_primary}")
|
||||
# Load model with device-specific logic
|
||||
try:
|
||||
if args.device == "mps":
|
||||
model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
|
||||
args.model_path,
|
||||
torch_dtype=load_dtype,
|
||||
attn_implementation=attn_impl_primary,
|
||||
device_map=None, # load then move
|
||||
)
|
||||
model.to("mps")
|
||||
elif args.device == "cuda":
|
||||
model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
|
||||
args.model_path,
|
||||
torch_dtype=load_dtype,
|
||||
device_map="cuda",
|
||||
attn_implementation=attn_impl_primary,
|
||||
)
|
||||
else: # cpu
|
||||
model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
|
||||
args.model_path,
|
||||
torch_dtype=load_dtype,
|
||||
device_map="cpu",
|
||||
attn_implementation=attn_impl_primary,
|
||||
)
|
||||
except Exception as e:
|
||||
if attn_impl_primary == 'flash_attention_2':
|
||||
print(f"[ERROR] : {type(e).__name__}: {e}")
|
||||
print(traceback.format_exc())
|
||||
print("Error loading the model. Trying to use SDPA. However, note that only flash_attention_2 has been fully tested, and using SDPA may result in lower audio quality.")
|
||||
model = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
|
||||
args.model_path,
|
||||
torch_dtype=load_dtype,
|
||||
device_map=(args.device if args.device in ("cuda", "cpu") else None),
|
||||
attn_implementation='sdpa'
|
||||
)
|
||||
if args.device == "mps":
|
||||
model.to("mps")
|
||||
else:
|
||||
raise e
|
||||
|
||||
|
||||
model.eval()
|
||||
model.set_ddpm_inference_steps(num_steps=5)
|
||||
|
||||
if hasattr(model.model, 'language_model'):
|
||||
print(f"Language model attention: {model.model.language_model.config._attn_implementation}")
|
||||
|
||||
target_device = args.device if args.device != "cpu" else "cpu"
|
||||
voice_sample = voice_mapper.get_voice_path(args.speaker_name)
|
||||
all_prefilled_outputs = torch.load(voice_sample, map_location=target_device, weights_only=False)
|
||||
|
||||
# Prepare inputs for the model
|
||||
inputs = processor.process_input_with_cached_prompt(
|
||||
text=full_script,
|
||||
cached_prompt=all_prefilled_outputs,
|
||||
padding=True,
|
||||
return_tensors="pt",
|
||||
return_attention_mask=True,
|
||||
)
|
||||
|
||||
# Move tensors to target device
|
||||
for k, v in inputs.items():
|
||||
if torch.is_tensor(v):
|
||||
inputs[k] = v.to(target_device)
|
||||
|
||||
print(f"Starting generation with cfg_scale: {args.cfg_scale}")
|
||||
|
||||
# Generate audio
|
||||
start_time = time.time()
|
||||
outputs = model.generate(
|
||||
**inputs,
|
||||
max_new_tokens=None,
|
||||
cfg_scale=args.cfg_scale,
|
||||
tokenizer=processor.tokenizer,
|
||||
generation_config={'do_sample': False},
|
||||
verbose=True,
|
||||
all_prefilled_outputs=copy.deepcopy(all_prefilled_outputs) if all_prefilled_outputs is not None else None,
|
||||
)
|
||||
generation_time = time.time() - start_time
|
||||
print(f"Generation time: {generation_time:.2f} seconds")
|
||||
|
||||
# Calculate audio duration and additional metrics
|
||||
if outputs.speech_outputs and outputs.speech_outputs[0] is not None:
|
||||
# Assuming 24kHz sample rate (common for speech synthesis)
|
||||
sample_rate = 24000
|
||||
audio_samples = outputs.speech_outputs[0].shape[-1] if len(outputs.speech_outputs[0].shape) > 0 else len(outputs.speech_outputs[0])
|
||||
audio_duration = audio_samples / sample_rate
|
||||
rtf = generation_time / audio_duration if audio_duration > 0 else float('inf')
|
||||
|
||||
print(f"Generated audio duration: {audio_duration:.2f} seconds")
|
||||
print(f"RTF (Real Time Factor): {rtf:.2f}x")
|
||||
else:
|
||||
print("No audio output generated")
|
||||
|
||||
# Calculate token metrics
|
||||
input_tokens = inputs['tts_text_ids'].shape[1] # Number of input tokens
|
||||
output_tokens = outputs.sequences.shape[1] # Total tokens (input + generated)
|
||||
generated_tokens = output_tokens - input_tokens - all_prefilled_outputs['tts_lm']['last_hidden_state'].size(1)
|
||||
|
||||
print(f"Prefilling text tokens: {input_tokens}")
|
||||
print(f"Generated speech tokens: {generated_tokens}")
|
||||
print(f"Total tokens: {output_tokens}")
|
||||
|
||||
# Save output (processor handles device internally)
|
||||
txt_filename = os.path.splitext(os.path.basename(args.txt_path))[0]
|
||||
output_path = os.path.join(args.output_dir, f"{txt_filename}_generated.wav")
|
||||
os.makedirs(args.output_dir, exist_ok=True)
|
||||
|
||||
processor.save_audio(
|
||||
outputs.speech_outputs[0], # First (and only) batch item
|
||||
output_path=output_path,
|
||||
)
|
||||
print(f"Saved output to {output_path}")
|
||||
|
||||
# Print summary
|
||||
print("\n" + "="*50)
|
||||
print("GENERATION SUMMARY")
|
||||
print("="*50)
|
||||
print(f"Input file: {args.txt_path}")
|
||||
print(f"Output file: {output_path}")
|
||||
print(f"Speaker names: {args.speaker_name}")
|
||||
print(f"Prefilling text tokens: {input_tokens}")
|
||||
print(f"Generated speech tokens: {generated_tokens}")
|
||||
print(f"Total tokens: {output_tokens}")
|
||||
print(f"Generation time: {generation_time:.2f} seconds")
|
||||
print(f"Audio duration: {audio_duration:.2f} seconds")
|
||||
print(f"RTF (Real Time Factor): {rtf:.2f}x")
|
||||
|
||||
print("="*50)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user