deepgeek/CosyVoice
2
0
Fork 0
mirror of https://github.com/FunAudioLLM/CosyVoice.git synced 2026-02-05 18:09:24 +08:00
Code Issues Packages Projects Releases Wiki Activity

support streaming tts

Browse Source
This commit is contained in:
root
2025-09-02 18:32:12 +08:00
parent b7ec6c4678
commit 73d261dd48
4 changed files with 199 additions and 62 deletions
Download Patch File Download Diff File Expand all files Collapse all files

62
runtime/triton_trtllm/client_grpc.py
View File

@@ -395,38 +395,45 @@ def run_sync_streaming_inference(
# Reconstruct audio using cross-fade (from client_grpc_streaming.py)
actual_duration = 0
if audios:
cross_fade_samples = int(chunk_overlap_duration * save_sample_rate)
fade_out = np.linspace(1, 0, cross_fade_samples)
fade_in = np.linspace(0, 1, cross_fade_samples)
reconstructed_audio = None
# Only spark_tts model uses cross-fade
if model_name == "spark_tts":
cross_fade_samples = int(chunk_overlap_duration * save_sample_rate)
fade_out = np.linspace(1, 0, cross_fade_samples)
fade_in = np.linspace(0, 1, cross_fade_samples)
reconstructed_audio = None
# Simplified reconstruction based on client_grpc_streaming.py
if not audios:
print("Warning: No audio chunks received.")
reconstructed_audio = np.array([], dtype=np.float32) # Empty array
elif len(audios) == 1:
reconstructed_audio = audios[0]
# Simplified reconstruction based on client_grpc_streaming.py
if not audios:
print("Warning: No audio chunks received.")
reconstructed_audio = np.array([], dtype=np.float32) # Empty array
elif len(audios) == 1:
reconstructed_audio = audios[0]
else:
reconstructed_audio = audios[0][:-cross_fade_samples] # Start with first chunk minus overlap
for i in range(1, len(audios)):
# Cross-fade section
cross_faded_overlap = (audios[i][:cross_fade_samples] * fade_in +
audios[i - 1][-cross_fade_samples:] * fade_out)
# Middle section of the current chunk
middle_part = audios[i][cross_fade_samples:-cross_fade_samples]
# Concatenate
reconstructed_audio = np.concatenate([reconstructed_audio, cross_faded_overlap, middle_part])
# Add the last part of the final chunk
reconstructed_audio = np.concatenate([reconstructed_audio, audios[-1][-cross_fade_samples:]])
if reconstructed_audio is not None and reconstructed_audio.size > 0:
actual_duration = len(reconstructed_audio) / save_sample_rate
# Save reconstructed audio
sf.write(audio_save_path, reconstructed_audio, save_sample_rate, "PCM_16")
else:
print("Warning: No audio chunks received or reconstructed.")
actual_duration = 0 # Set duration to 0 if no audio
else:
reconstructed_audio = audios[0][:-cross_fade_samples] # Start with first chunk minus overlap
for i in range(1, len(audios)):
# Cross-fade section
cross_faded_overlap = (audios[i][:cross_fade_samples] * fade_in +
audios[i - 1][-cross_fade_samples:] * fade_out)
# Middle section of the current chunk
middle_part = audios[i][cross_fade_samples:-cross_fade_samples]
# Concatenate
reconstructed_audio = np.concatenate([reconstructed_audio, cross_faded_overlap, middle_part])
# Add the last part of the final chunk
reconstructed_audio = np.concatenate([reconstructed_audio, audios[-1][-cross_fade_samples:]])
if reconstructed_audio is not None and reconstructed_audio.size > 0:
reconstructed_audio = np.concatenate(audios)
print(f"reconstructed_audio: {reconstructed_audio.shape}")
actual_duration = len(reconstructed_audio) / save_sample_rate
# Save reconstructed audio
os.makedirs(os.path.dirname(audio_save_path), exist_ok=True)
sf.write(audio_save_path, reconstructed_audio, save_sample_rate, "PCM_16")
else:
print("Warning: No audio chunks received or reconstructed.")
actual_duration = 0 # Set duration to 0 if no audio
else:
print("Warning: No audio chunks received.")
@@ -667,6 +674,7 @@ async def main():
manifest_item_list = split_data(manifest_item_list, num_tasks)
os.makedirs(args.log_dir, exist_ok=True)
tasks = []
start_time = time.time()
for i in range(num_tasks):