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feat: use xpu as default if it exists

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Ayo Ayco 2025-09-02 23:13:40 +02:00
parent 6065c5224e
commit 66b32b2e4e
2 changed files with 49 additions and 44 deletions
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5
.gitignore vendored
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@ -169,9 +169,14 @@ tags
.ruff_cache
# our proj
/inputs/
/output/
/outputs/
/checkpoint/
/checkpoints/
exp
.gradio/
*~
*swp
*swo

88
demo/inference_from_file.py
View file

@ -16,17 +16,17 @@ 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 wav 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]
@ -37,21 +37,21 @@ class VoiceMapper:
def setup_voice_presets(self):
"""Setup voice presets by scanning the voices directory."""
voices_dir = os.path.join(os.path.dirname(__file__), "voices")
# 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 WAV files in the voices directory
self.voice_presets = {}
# Get all .wav files in the voices directory
wav_files = [f for f in os.listdir(voices_dir)
wav_files = [f for f in os.listdir(voices_dir)
if f.lower().endswith('.wav') and os.path.isfile(os.path.join(voices_dir, f))]
# Create dictionary with filename (without extension) as key
for wav_file in wav_files:
# Remove .wav extension to get the name
@ -59,16 +59,16 @@ class VoiceMapper:
# Create full path
full_path = os.path.join(voices_dir, wav_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())}")
@ -77,13 +77,13 @@ class VoiceMapper:
# 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}")
@ -99,25 +99,25 @@ def parse_txt_script(txt_content: str) -> Tuple[List[str], List[str]]:
lines = txt_content.strip().split('\n')
scripts = []
speaker_numbers = []
# Pattern to match "Speaker X:" format where X is a number
speaker_pattern = r'^Speaker\s+(\d+):\s*(.*)$'
current_speaker = None
current_text = ""
for line in lines:
line = line.strip()
if not line:
continue
match = re.match(speaker_pattern, line, re.IGNORECASE)
if match:
# If we have accumulated text from previous speaker, save it
if current_speaker and current_text:
scripts.append(f"Speaker {current_speaker}: {current_text.strip()}")
speaker_numbers.append(current_speaker)
# Start new speaker
current_speaker = match.group(1).strip()
current_text = match.group(2).strip()
@ -127,12 +127,12 @@ def parse_txt_script(txt_content: str) -> Tuple[List[str], List[str]]:
current_text += " " + line
else:
current_text = line
# Don't forget the last speaker
if current_speaker and current_text:
scripts.append(f"Speaker {current_speaker}: {current_text.strip()}")
speaker_numbers.append(current_speaker)
return scripts, speaker_numbers
@ -144,7 +144,7 @@ def parse_args():
default="microsoft/VibeVoice-1.5b",
help="Path to the HuggingFace model directory",
)
parser.add_argument(
"--txt_path",
type=str,
@ -167,7 +167,7 @@ def parse_args():
parser.add_argument(
"--device",
type=str,
default=("cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")),
default=("cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else ("xpu" if torch.xpu.is_available() else "cpu"))),
help="Device for inference: cuda | mps | cpu",
)
parser.add_argument(
@ -176,7 +176,7 @@ def parse_args():
default=1.3,
help="CFG (Classifier-Free Guidance) scale for generation (default: 1.3)",
)
return parser.parse_args()
def main():
@ -196,44 +196,44 @@ def main():
# 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:
txt_content = f.read()
# Parse the txt content to get speaker numbers
scripts, speaker_numbers = parse_txt_script(txt_content)
if not scripts:
print("Error: No valid speaker scripts found in the txt file")
return
print(f"Found {len(scripts)} speaker segments:")
for i, (script, speaker_num) in enumerate(zip(scripts, speaker_numbers)):
print(f" {i+1}. Speaker {speaker_num}")
print(f" Text preview: {script[:100]}...")
# Map speaker numbers to provided speaker names
speaker_name_mapping = {}
speaker_names_list = args.speaker_names if isinstance(args.speaker_names, list) else [args.speaker_names]
for i, name in enumerate(speaker_names_list, 1):
speaker_name_mapping[str(i)] = name
print(f"\nSpeaker mapping:")
for speaker_num in set(speaker_numbers):
mapped_name = speaker_name_mapping.get(speaker_num, f"Speaker {speaker_num}")
print(f" Speaker {speaker_num} -> {mapped_name}")
# Map speakers to voice files using the provided speaker names
voice_samples = []
actual_speakers = []
# Get unique speaker numbers in order of first appearance
unique_speaker_numbers = []
seen = set()
@ -241,18 +241,18 @@ def main():
if speaker_num not in seen:
unique_speaker_numbers.append(speaker_num)
seen.add(speaker_num)
for speaker_num in unique_speaker_numbers:
speaker_name = speaker_name_mapping.get(speaker_num, f"Speaker {speaker_num}")
voice_path = voice_mapper.get_voice_path(speaker_name)
voice_samples.append(voice_path)
actual_speakers.append(speaker_name)
print(f"Speaker {speaker_num} ('{speaker_name}') -> Voice: {os.path.basename(voice_path)}")
# Prepare data for model
full_script = '\n'.join(scripts)
full_script = full_script.replace("’", "'")
full_script = full_script.replace("’", "'")
print(f"Loading processor & model from {args.model_path}")
processor = VibeVoiceProcessor.from_pretrained(args.model_path)
@ -314,7 +314,7 @@ def main():
if hasattr(model.model, 'language_model'):
print(f"Language model attention: {model.model.language_model.config._attn_implementation}")
# Prepare inputs for the model
inputs = processor(
text=[full_script], # Wrap in list for batch processing
@ -344,7 +344,7 @@ def main():
)
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)
@ -352,17 +352,17 @@ def main():
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['input_ids'].shape[1] # Number of input tokens
output_tokens = outputs.sequences.shape[1] # Total tokens (input + generated)
generated_tokens = output_tokens - input_tokens
print(f"Prefilling tokens: {input_tokens}")
print(f"Generated tokens: {generated_tokens}")
print(f"Total tokens: {output_tokens}")
@ -371,13 +371,13 @@ def main():
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")
@ -393,7 +393,7 @@ def main():
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__":