nodes/SayanoAI-Comfy-RVC
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

added frames padding

Browse Source
This commit is contained in:
SayanoAI
2024-07-31 18:28:34 -04:00
parent 02f3b4226c
commit f91f647e7e
1 changed files with 7 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files
custom_nodes/stt.py
+7 -5
View File
@@ -163,7 +163,8 @@ class BatchedTranscriptionEncoderNode:
"prefix": ("STRING", {"default": "masterpiece, best quality", "multiline": True, "forceInput": True}),
"suffix": ("STRING", {"default": "", "multiline": True, "forceInput": True}),
"print_output": ('BOOLEAN', {'default': True}),
"weights": ("FLOAT",{"default": 1., "step": .01})
"weights": ("FLOAT",{"default": 1., "step": .01}),
"pad_frames": ("INT", {"default": 0})
}
}
@@ -214,12 +215,13 @@ class BatchedTranscriptionEncoderNode:
def get_prompt(
self, transcription, clip, language="en", loop=False, use_tags=False, use_sentiment=False,
max_words=16,max_chunks=None, frame_interpolation=0, print_output=True, prefix="", suffix="", weights=1.
max_words=16,max_chunks=None, frame_interpolation=0, print_output=True, prefix="", suffix="",
weights=1., pad_frames=0
):
if not max_chunks: max_chunks = len(transcription['chunks'])
total_chunks = transcription['chunks'][:max_chunks]
max_frames = max(max_chunks, *filter(None,np.array([chunk["timestamp"] for chunk in total_chunks]).flatten()))
max_frames = max(max_chunks, *filter(None,np.array([chunk["timestamp"] for chunk in total_chunks]).flatten())) + pad_frames
last_chunk = None
for i in range(len(total_chunks)):
@@ -232,7 +234,7 @@ class BatchedTranscriptionEncoderNode:
if last_chunk is not None:
timestamp = last_chunk["timestamp"]
start_time = timestamp[-1 if loop else 0]
end_time = start_time + max(max_frames - start_time,0)+1
end_time = start_time + max(max_frames - start_time,0)
timestamp = (start_time,end_time)
last_chunk = dict(timestamp=timestamp,text=total_chunks[0 if loop else -1]["text"])
@@ -265,7 +267,7 @@ class BatchedTranscriptionEncoderNode:
num_chunks = len(total_chunks)
duration_list = np.round(duration_list)
num_frames = int(np.sum(duration_list))+1
num_frames = int(np.sum(duration_list))
final_pooled_output = torch.nested.to_padded_tensor(torch.nested.nested_tensor(pooled, dtype=torch.float32),0)
final_conditioning = torch.nested.to_padded_tensor(torch.nested.nested_tensor(cond, dtype=torch.float32),0)
print(f"{final_conditioning.shape=} {final_pooled_output.shape=}")