Repository: yuvraj108c/ComfyUI-Whisper
Branch: master
Commit: 74aa5a217b1c
Files: 14
Total size: 64.3 KB
Directory structure:
gitextract_roxluuhh/
├── .github/
│ └── workflows/
│ └── publish.yml
├── .gitignore
├── LICENSE
├── __init__.py
├── add_subtitles_to_background.py
├── add_subtitles_to_frames.py
├── apply_whisper.py
├── example_workflows/
│ └── whisper_video_subtitles_workflow.json
├── pyproject.toml
├── readme.md
├── requirements.txt
├── resize_cropped_subtitles.py
├── save_srt.py
└── utils.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/workflows/publish.yml
================================================
name: Publish to Comfy registry
on:
workflow_dispatch:
push:
branches:
- main
paths:
- "pyproject.toml"
jobs:
publish-node:
name: Publish Custom Node to registry
runs-on: ubuntu-latest
steps:
- name: Check out code
uses: actions/checkout@v4
- name: Publish Custom Node
uses: Comfy-Org/publish-node-action@main
with:
## Add your own personal access token to your Github Repository secrets and reference it here.
personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
================================================
FILE: .gitignore
================================================
__pycache__
.DS_Store
================================================
FILE: LICENSE
================================================
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================================================
FILE: __init__.py
================================================
from .apply_whisper import ApplyWhisperNode
from .add_subtitles_to_frames import AddSubtitlesToFramesNode
from .add_subtitles_to_background import AddSubtitlesToBackgroundNode
from .resize_cropped_subtitles import ResizeCroppedSubtitlesNode
from .save_srt import SaveSRTNode
NODE_CLASS_MAPPINGS = {
"Apply Whisper" : ApplyWhisperNode,
"Add Subtitles To Frames": AddSubtitlesToFramesNode,
"Add Subtitles To Background": AddSubtitlesToBackgroundNode,
"Resize Cropped Subtitles": ResizeCroppedSubtitlesNode,
"Save SRT": SaveSRTNode
}
NODE_DISPLAY_NAME_MAPPINGS = {
"Apply Whisper" : "Apply Whisper",
"Add Subtitles To Frames": "Add Subtitles To Frames",
"Add Subtitles To Background": "Add Subtitles To Background",
"Resize Cropped Subtitles": "Resize Cropped Subtitles",
"Save SRT": "Save SRT"
}
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
================================================
FILE: add_subtitles_to_background.py
================================================
from PIL import ImageDraw, ImageFont, Image
from .utils import pil2tensor,tensor2pil
import math
import os
import random
FONT_DIR = os.path.join(os.path.dirname(__file__),"fonts")
class AddSubtitlesToBackgroundNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
"alignment" : ("whisper_alignment",),
"font_family": (os.listdir(FONT_DIR),),
"num_words": ("INT",{
"default": 25,
"step":1,
"display": "number"
}),
"text_displacement": ("INT",{
"default": 10,
"step":1,
"display": "number"
}),
"font_size_displacement": ("INT",{
"default": 3,
"step":1,
"display": "number"
}),
"min_font_size": ("INT",{
"default": 15,
"step":1,
"display": "number"
}),
"max_font_size": ("INT",{
"default": 75,
"step":1,
"display": "number"
}),
"video_fps": ("FLOAT",{
"default": 24.0,
"step":1,
"display": "number"
}),
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "add_subtitles_to_background"
CATEGORY = "whisper"
def add_subtitles_to_background(self, images, alignment, font_family, text_displacement, font_size_displacement,num_words, min_font_size, max_font_size, video_fps):
pil_images = tensor2pil(images)
frame_width, frame_height = pil_images[0].size
bg_image = Image.new("RGB", (frame_width, frame_height), (255, 255, 255))
background_color = (0, 0, 0)
text_color = (255,255,255)
# Randomly scatter the initial (x, y) positions and font sizes within the image size
positions = [(random.randint(0, frame_width - 100), random.randint(0, frame_height - 30)) for _ in range(num_words)]
# create N font sizes in defined range
font_sizes = [random.randint(min_font_size, max_font_size) for _ in range(100)]
final_pil_images = []
if len(alignment) == 0:
bg_image = Image.new("RGB", (frame_width, frame_height), background_color)
final_pil_images.extend([bg_image]*len(pil_images))
last_frame_no = 0
for x in range(len(alignment)):
alignment_obj = alignment[x]
start_frame_no = math.floor(alignment_obj["start"] * video_fps)
end_frame_no = math.floor(alignment_obj["end"] * video_fps)
word = alignment_obj["value"]
# create images with no texts
for _ in range(last_frame_no, start_frame_no):
bg_image = Image.new("RGB", (frame_width, frame_height), background_color)
final_pil_images.append(bg_image)
for _ in range(start_frame_no,end_frame_no):
# Create a blank frame with background color
bg_image = Image.new("RGB", (frame_width, frame_height), background_color)
draw = ImageDraw.Draw(bg_image)
# Create new lists to store the updated positions and font sizes
updated_positions = []
updated_font_sizes = []
# Loop to add text at (x, y) positions and sizes without overlapping
for i, pos in enumerate(positions):
x, y = pos # Unpack the (x, y) position from the tuple
# Randomly choose a direction (up, down, left, or right) and apply displacement
direction = random.choice(["up", "down", "left", "right"])
if direction == "up":
y -= text_displacement
elif direction == "down":
y += text_displacement
elif direction == "left":
x -= text_displacement
elif direction == "right":
x += text_displacement
# Ensure that the new (x, y) positions stay within the image boundaries
x = max(0, min(x, frame_width - 100))
y = max(0, min(y, frame_height - 30))
# Randomly add/subtract X pixels from the font size
font_size = font_sizes[i] + random.choice([-font_size_displacement, font_size_displacement])
font_size = int(max(min_font_size, min(font_size, max_font_size))) # Ensure font size is within the desired range
# Calculate the text bounding box
font = ImageFont.truetype(os.path.join(FONT_DIR,font_family), size=font_size)
text_bbox = draw.textbbox((x, y), word, font=font)
# Collision detection: Check if the current text box intersects with any previously added text boxes
overlap = any(
(
x1 < text_bbox[2] and x2 > text_bbox[0] and
y1 < text_bbox[3] and y2 > text_bbox[1]
)
for x1, y1, x2, y2 in updated_positions
)
# Use a while loop to keep trying to place the word until no overlap is detected
while overlap:
# Randomly adjust the position and font size
x, y = random.randint(0, frame_width - 100), random.randint(0, frame_height - 30)
font_size = int(random.randint(min_font_size, max_font_size))
# Recalculate the text bounding box
font = ImageFont.truetype(os.path.join(FONT_DIR,font_family), size=font_size)
text_bbox = draw.textbbox((x, y), word, font=font)
# Check for overlap again
overlap = any(
(
x1 < text_bbox[2] and x2 > text_bbox[0] and
y1 < text_bbox[3] and y2 > text_bbox[1]
)
for x1, y1, x2, y2 in updated_positions
)
# Add the word to the frame and update the used positions and font sizes lists
draw.text((x, y), word, fill=text_color, font=font)
updated_positions.append((x, y, text_bbox[2], text_bbox[3]))
updated_font_sizes.append(font_size)
# Update the positions and font sizes lists with the new positions and font sizes
positions = [(x1, y1) for x1, y1, _, _ in updated_positions]
font_sizes = updated_font_sizes
final_pil_images.append(bg_image)
bg_image = Image.new("RGB", (frame_width, frame_height), text_color)
last_frame_no = end_frame_no
# create missing black images at the end
missing_frames_count = len(pil_images) - len(final_pil_images)
for _ in range(missing_frames_count):
bg_image = Image.new("RGB", (frame_width, frame_height), background_color)
final_pil_images.append(bg_image)
tensor_images = pil2tensor(final_pil_images)
return (tensor_images,)
================================================
FILE: add_subtitles_to_frames.py
================================================
from PIL import ImageDraw, ImageFont, Image
from .utils import tensor2pil, pil2tensor, tensor2Mask
import math
import os
FONT_DIR = os.path.join(os.path.dirname(__file__),"fonts")
class AddSubtitlesToFramesNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE",),
"alignment" : ("whisper_alignment",),
"font_color": ("STRING",{
"default": "white"
}),
"font_family": (os.listdir(FONT_DIR),),
"font_size": ("INT",{
"default": 100,
"step":5,
"display": "number"
}),
"x_position": ("INT",{
"default": 100,
"step":50,
"display": "number"
}),
"y_position": ("INT",{
"default": 100,
"step":50,
"display": "number"
}),
"center_x": ("BOOLEAN", {"default": True}),
"center_y": ("BOOLEAN", {"default": True}),
"video_fps": ("FLOAT",{
"default": 24.0,
"step":1,
"display": "number"
}),
}
}
RETURN_TYPES = ("IMAGE", "MASK", "IMAGE", "subtitle_coord", )
RETURN_NAMES = ("IMAGE","MASK", "cropped_subtitles","subtitle_coord",)
FUNCTION = "add_subtitles_to_frames"
CATEGORY = "whisper"
def add_subtitles_to_frames(self, images, alignment, font_family, font_size, font_color, x_position, y_position, center_x, center_y, video_fps):
pil_images = tensor2pil(images)
pil_images_with_text = []
cropped_pil_images_with_text = []
pil_images_masks = []
subtitle_coord = []
font = ImageFont.truetype(os.path.join(FONT_DIR,font_family), font_size)
if len(alignment) == 0:
pil_images_with_text = pil_images
cropped_pil_images_with_text = pil_images
subtitle_coord.extend([(0,0,0,0)]*len(pil_images))
# create mask
width, height = pil_images[0].size
black_img = Image.new('RGB', (width, height), 'black')
pil_images_masks.extend([black_img]*len(pil_images))
last_frame_no = 0
for i in range(len(alignment)):
alignment_obj = alignment[i]
start_frame_no = math.floor(alignment_obj["start"] * video_fps)
end_frame_no = math.floor(alignment_obj["end"] * video_fps)
# create images without text
for i in range(last_frame_no, start_frame_no):
img = pil_images[i].convert("RGB")
width, height = img.size
pil_images_with_text.append(img)
# create mask + cropped image
black_img = Image.new('RGB', (width, height), 'black')
pil_images_masks.append(black_img)
black_img = Image.new('RGB', (1, 1), 'black') # to prevent max() from considering these images, use very small size
cropped_pil_images_with_text.append(black_img)
subtitle_coord.append((0,0,0,0))
for i in range(start_frame_no,end_frame_no):
img = pil_images[i].convert("RGB")
width, height = img.size
d = ImageDraw.Draw(img)
# center text
text_bbox = d.textbbox((x_position, y_position), alignment_obj["value"], font=font)
if center_x:
text_width = text_bbox[2] - text_bbox[0]
x_position = (width - text_width)/2
if center_y:
text_height = text_bbox[3] - text_bbox[1]
y_position = (height - text_height)/2
# add text to video frames
d.text((x_position, y_position), alignment_obj["value"], fill=font_color,font=font)
pil_images_with_text.append(img)
# create mask
black_img = Image.new('RGB', (width, height), 'black')
d = ImageDraw.Draw(black_img)
d.text((x_position, y_position), alignment_obj["value"], fill="white",font=font)
pil_images_masks.append(black_img)
# crop subtitles to black frame
text_bbox = d.textbbox((x_position,y_position), alignment_obj["value"], font=font)
cropped_text_frame = black_img.crop(text_bbox)
cropped_pil_images_with_text.append(cropped_text_frame)
subtitle_coord.append(text_bbox)
last_frame_no = end_frame_no
# add missing frames with no text at end
for i in range(len(pil_images_with_text),len(pil_images)):
pil_images_with_text.append(pil_images[i])
width,height = pil_images[i].size
# create mask + cropped image
black_img = Image.new('RGB', (width, height), 'black')
pil_images_masks.append(black_img)
black_img = Image.new('RGB', (1, 1), 'black') # to prevent max() from considering these images, use very small size
cropped_pil_images_with_text.append(black_img)
subtitle_coord.append((0,0,0,0))
# make cropped images same size
cropped_pil_images_with_text_normalised = []
max_width = max(img.width for img in cropped_pil_images_with_text)
max_height = max(img.height for img in cropped_pil_images_with_text)
for img in cropped_pil_images_with_text:
blank_frame = Image.new("RGB", (max_width, max_height), "black")
blank_frame.paste(img, (0,0))
cropped_pil_images_with_text_normalised.append(blank_frame)
tensor_images = pil2tensor(pil_images_with_text)
cropped_pil_images_with_text_normalised = pil2tensor(cropped_pil_images_with_text_normalised)
tensor_masks = tensor2Mask(pil2tensor(pil_images_masks))
return (tensor_images,tensor_masks,cropped_pil_images_with_text_normalised,subtitle_coord,)
================================================
FILE: apply_whisper.py
================================================
import whisper
import os
import folder_paths
import uuid
import torchaudio
import torch
import logging
import comfy.model_management as mm
import comfy.model_patcher
WHISPER_MODEL_SUBDIR = os.path.join("stt", "whisper")
logger = logging.getLogger(__name__)
WHISPER_PATCHER_CACHE = {}
class WhisperModelWrapper(torch.nn.Module):
"""
A torch.nn.Module wrapper for Whisper models.
This allows ComfyUI's model management to treat Whisper models like any other
torch module, enabling device placement and memory management.
"""
def __init__(self, model_name, download_root):
super().__init__()
self.model_name = model_name
self.download_root = download_root
self.whisper_model = None
self.model_loaded_weight_memory = 0
def load_model(self, device):
"""Load the Whisper model from disk to the specified device"""
self.whisper_model = whisper.load_model(
self.model_name,
download_root=self.download_root,
device=device
)
# Estimate model size for memory management
model_size = sum(p.numel() * p.element_size() for p in self.whisper_model.parameters())
self.model_loaded_weight_memory = model_size
class WhisperPatcher(comfy.model_patcher.ModelPatcher):
"""
Custom ModelPatcher for Whisper models that integrates with ComfyUI's
model management system for proper loading/offloading.
"""
def __init__(self, model, *args, **kwargs):
super().__init__(model, *args, **kwargs)
def patch_model(self, device_to=None, *args, **kwargs):
"""
This method is called by ComfyUI's model manager when it's time to load
the model onto the target device (usually the GPU). Our responsibility here
is to ensure the model weights are loaded from disk if they haven't been already.
"""
target_device = self.load_device
if self.model.whisper_model is None:
logger.info(f"Loading Whisper model '{self.model.model_name}' to {target_device}...")
self.model.load_model(target_device)
self.size = self.model.model_loaded_weight_memory
else:
logger.info(f"Whisper model '{self.model.model_name}' already in memory.")
return super().patch_model(device_to=target_device, *args, **kwargs)
def unpatch_model(self, device_to=None, unpatch_weights=True, *args, **kwargs):
"""
Offload the Whisper model to free up VRAM.
"""
if unpatch_weights:
logger.info(f"Offloading Whisper model '{self.model.model_name}' to {device_to}...")
self.model.whisper_model = None
self.model.model_loaded_weight_memory = 0
mm.soft_empty_cache()
return super().unpatch_model(device_to, unpatch_weights, *args, **kwargs)
class ApplyWhisperNode:
languages_by_name = None
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"audio": ("AUDIO",),
"model": (['tiny.en', 'tiny', 'base.en', 'base', 'small.en', 'small', 'medium.en', 'medium', 'large-v1', 'large-v2', 'large-v3', 'large', 'large-v3-turbo', 'turbo'],),
},
"optional": {
"language": (
["auto"] +
[s.capitalize() for s in sorted(list(whisper.tokenizer.LANGUAGES.values())) ],
),
"prompt": ("STRING", {"default":""}),
}
}
RETURN_TYPES = ("STRING", "whisper_alignment", "whisper_alignment")
RETURN_NAMES = ("text", "segments_alignment", "words_alignment")
FUNCTION = "apply_whisper"
CATEGORY = "whisper"
def apply_whisper(self, audio, model, language, prompt):
# save audio bytes from VHS to file
temp_dir = folder_paths.get_temp_directory()
os.makedirs(temp_dir, exist_ok=True)
audio_save_path = os.path.join(temp_dir, f"{uuid.uuid1()}.wav")
torchaudio.save(audio_save_path, audio['waveform'].squeeze(
0), audio["sample_rate"])
cache_key = model
if cache_key not in WHISPER_PATCHER_CACHE:
load_device = mm.get_torch_device()
download_root = os.path.join(folder_paths.models_dir, WHISPER_MODEL_SUBDIR)
logger.info(f"Creating Whisper ModelPatcher for {model} on device {load_device}")
model_wrapper = WhisperModelWrapper(model, download_root)
patcher = WhisperPatcher(
model=model_wrapper,
load_device=load_device,
offload_device=mm.unet_offload_device(),
size=0 # Will be set when model loads
)
WHISPER_PATCHER_CACHE[cache_key] = patcher
patcher = WHISPER_PATCHER_CACHE[cache_key]
mm.load_model_gpu(patcher)
whisper_model = patcher.model.whisper_model
if whisper_model is None:
logger.error("Whisper model failed to load. Please check logs for errors.")
raise RuntimeError(f"Failed to load Whisper model: {model}")
transcribe_args = {"initial_prompt": prompt}
if language != "auto":
if ApplyWhisperNode.languages_by_name is None:
ApplyWhisperNode.languages_by_name = {v.lower(): k for k, v in whisper.tokenizer.LANGUAGES.items()}
transcribe_args['language'] = ApplyWhisperNode.languages_by_name[language.lower()]
result = whisper_model.transcribe(audio_save_path, word_timestamps=True, **transcribe_args)
segments = result['segments']
segments_alignment = []
words_alignment = []
for segment in segments:
# create segment alignments
segment_dict = {
'value': segment['text'].strip(),
'start': segment['start'],
'end': segment['end']
}
segments_alignment.append(segment_dict)
# create word alignments
for word in segment["words"]:
word_dict = {
'value': word["word"].strip(),
'start': word["start"],
'end': word['end']
}
words_alignment.append(word_dict)
return (result["text"].strip(), segments_alignment, words_alignment)
================================================
FILE: example_workflows/whisper_video_subtitles_workflow.json
================================================
{
"id": "13ec9b05-13e3-4ce0-a274-dfe796b6c75d",
"revision": 0,
"last_node_id": 98,
"last_link_id": 212,
"nodes": [
{
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"type": "PreviewAny",
"pos": [
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],
"size": [
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],
"flags": {},
"order": 9,
"mode": 0,
"inputs": [
{
"name": "source",
"type": "*",
"link": 208
}
],
"outputs": [],
"title": "Preview Text",
"properties": {
"cnr_id": "comfy-core",
"ver": "0.7.0",
"Node name for S&R": "PreviewAny"
},
"widgets_values": [
null,
null,
null
]
},
{
"id": 94,
"type": "Save SRT",
"pos": [
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],
"size": [
270,
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],
"flags": {},
"order": 5,
"mode": 0,
"inputs": [
{
"name": "alignment",
"type": "whisper_alignment",
"link": 210
}
],
"outputs": [
{
"name": "srt_path",
"type": "STRING",
"links": [
208
]
}
],
"properties": {
"cnr_id": "ComfyUI-Whisper",
"ver": "c67c040559c013833dedb0737df14182dc5043cc",
"Node name for S&R": "Save SRT"
},
"widgets_values": [
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]
},
{
"id": 97,
"type": "PreviewAny",
"pos": [
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],
"size": [
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],
"flags": {},
"order": 10,
"mode": 0,
"inputs": [
{
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"link": 209
}
],
"outputs": [],
"title": "Preview Text",
"properties": {
"cnr_id": "comfy-core",
"ver": "0.7.0",
"Node name for S&R": "PreviewAny"
},
"widgets_values": [
null,
null,
null
]
},
{
"id": 95,
"type": "Save SRT",
"pos": [
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],
"size": [
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],
"flags": {},
"order": 6,
"mode": 0,
"inputs": [
{
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"type": "whisper_alignment",
"link": 211
}
],
"outputs": [
{
"name": "srt_path",
"type": "STRING",
"links": [
209
]
}
],
"properties": {
"cnr_id": "ComfyUI-Whisper",
"ver": "c67c040559c013833dedb0737df14182dc5043cc",
"Node name for S&R": "Save SRT"
},
"widgets_values": [
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]
},
{
"id": 46,
"type": "VHS_LoadVideo",
"pos": [
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],
"size": [
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],
"flags": {},
"order": 0,
"mode": 0,
"inputs": [
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"type": "VHS_BatchManager",
"link": null
},
{
"name": "vae",
"shape": 7,
"type": "VAE",
"link": null
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"slot_index": 0,
"links": [
200
]
},
{
"name": "frame_count",
"type": "INT",
"slot_index": 1,
"links": []
},
{
"name": "audio",
"type": "AUDIO",
"slot_index": 2,
"links": [
104,
205
]
},
{
"name": "video_info",
"type": "VHS_VIDEOINFO",
"links": [
201
]
}
],
"properties": {
"cnr_id": "comfyui-videohelpersuite",
"ver": "1.7.9",
"Node name for S&R": "VHS_LoadVideo"
},
"widgets_values": {
"video": "sample.mp4",
"force_rate": 0,
"custom_width": 0,
"custom_height": 0,
"frame_load_cap": 0,
"skip_first_frames": 0,
"select_every_nth": 1,
"format": "AnimateDiff",
"videopreview": {
"hidden": false,
"paused": false,
"params": {
"frame_load_cap": 0,
"skip_first_frames": 0,
"force_rate": 0,
"filename": "sample.mp4",
"type": "input",
"format": "video/mp4",
"select_every_nth": 1
}
}
}
},
{
"id": 47,
"type": "Apply Whisper",
"pos": [
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-223.6544277301129
],
"size": [
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146
],
"flags": {},
"order": 1,
"mode": 0,
"inputs": [
{
"name": "audio",
"type": "AUDIO",
"link": 205
}
],
"outputs": [
{
"name": "text",
"type": "STRING",
"slot_index": 0,
"links": [
206
]
},
{
"name": "segments_alignment",
"type": "whisper_alignment",
"links": [
207,
210
]
},
{
"name": "words_alignment",
"type": "whisper_alignment",
"slot_index": 2,
"links": [
126,
211,
212
]
}
],
"properties": {
"cnr_id": "ComfyUI-Whisper",
"ver": "c67c040559c013833dedb0737df14182dc5043cc",
"Node name for S&R": "Apply Whisper",
"aux_id": "yuvraj108c/ComfyUI-Whisper"
},
"widgets_values": [
"large",
"auto",
""
]
},
{
"id": 89,
"type": "VHS_VideoInfoLoaded",
"pos": [
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],
"size": [
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],
"flags": {
"collapsed": true
},
"order": 2,
"mode": 0,
"inputs": [
{
"name": "video_info",
"type": "VHS_VIDEOINFO",
"link": 201
}
],
"outputs": [
{
"name": "fps🟦",
"type": "FLOAT",
"links": [
202,
203
]
},
{
"name": "frame_count🟦",
"type": "INT",
"links": null
},
{
"name": "duration🟦",
"type": "FLOAT",
"links": null
},
{
"name": "width🟦",
"type": "INT",
"links": null
},
{
"name": "height🟦",
"type": "INT",
"links": null
}
],
"properties": {
"cnr_id": "comfyui-videohelpersuite",
"ver": "1.7.9",
"Node name for S&R": "VHS_VideoInfoLoaded"
},
"widgets_values": {}
},
{
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],
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"mode": 0,
"inputs": [
{
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"type": "*",
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}
],
"outputs": [],
"title": "Preview Alignments",
"properties": {
"cnr_id": "comfy-core",
"ver": "0.7.0",
"Node name for S&R": "PreviewAny"
},
"widgets_values": [
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null,
null
]
},
{
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],
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}
],
"outputs": [],
"title": "Preview Alignments",
"properties": {
"cnr_id": "comfy-core",
"ver": "0.7.0",
"Node name for S&R": "PreviewAny"
},
"widgets_values": [
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null,
null
]
},
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"type": "PreviewAny",
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],
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],
"flags": {},
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"inputs": [
{
"name": "source",
"type": "*",
"link": 206
}
],
"outputs": [],
"title": "Preview Text",
"properties": {
"cnr_id": "comfy-core",
"ver": "0.7.0",
"Node name for S&R": "PreviewAny"
},
"widgets_values": [
null,
null,
null
]
},
{
"id": 61,
"type": "Add Subtitles To Frames",
"pos": [
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],
"size": [
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],
"flags": {},
"order": 8,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 200
},
{
"name": "alignment",
"type": "whisper_alignment",
"link": 126
},
{
"name": "video_fps",
"type": "FLOAT",
"widget": {
"name": "video_fps"
},
"link": 202
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"slot_index": 0,
"links": [
128
]
},
{
"name": "MASK",
"type": "MASK",
"slot_index": 1,
"links": []
},
{
"name": "cropped_subtitles",
"type": "IMAGE",
"links": null
},
{
"name": "subtitle_coord",
"type": "subtitle_coord",
"links": null
}
],
"properties": {
"cnr_id": "ComfyUI-Whisper",
"ver": "c67c040559c013833dedb0737df14182dc5043cc",
"Node name for S&R": "Add Subtitles To Frames",
"aux_id": "yuvraj108c/ComfyUI-Whisper"
},
"widgets_values": [
"red",
"YRDZST Semibold.ttf",
200,
100,
600,
24,
true,
24
]
},
{
"id": 49,
"type": "VHS_VideoCombine",
"pos": [
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],
"size": [
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],
"flags": {},
"order": 11,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 128
},
{
"name": "audio",
"shape": 7,
"type": "AUDIO",
"link": 104
},
{
"name": "meta_batch",
"shape": 7,
"type": "VHS_BatchManager",
"link": null
},
{
"name": "vae",
"shape": 7,
"type": "VAE",
"link": null
},
{
"name": "frame_rate",
"type": "FLOAT",
"widget": {
"name": "frame_rate"
},
"link": 203
}
],
"outputs": [
{
"name": "Filenames",
"type": "VHS_FILENAMES",
"links": null
}
],
"properties": {
"cnr_id": "comfyui-videohelpersuite",
"ver": "1.7.9",
"Node name for S&R": "VHS_VideoCombine"
},
"widgets_values": {
"frame_rate": 24,
"loop_count": 0,
"filename_prefix": "AnimateDiff",
"format": "video/h264-mp4",
"pix_fmt": "yuv420p",
"crf": 19,
"save_metadata": true,
"trim_to_audio": false,
"pingpong": false,
"save_output": true,
"videopreview": {
"hidden": false,
"paused": false,
"params": {
"filename": "AnimateDiff_00002-audio.mp4",
"subfolder": "",
"type": "output",
"format": "video/h264-mp4",
"frame_rate": 24,
"workflow": "AnimateDiff_00002.png",
"fullpath": "/workspace/ComfyUI/output/AnimateDiff_00002-audio.mp4"
}
}
}
}
],
"links": [
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46,
2,
49,
1,
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],
[
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2,
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1,
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],
[
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0,
49,
0,
"IMAGE"
],
[
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0,
"IMAGE"
],
[
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"VHS_VIDEOINFO"
],
[
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"FLOAT"
],
[
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"FLOAT"
],
[
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"AUDIO"
],
[
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0,
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0,
"*"
],
[
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],
[
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"STRING"
],
[
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],
[
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],
[
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0,
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],
[
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2,
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0,
"whisper_alignment"
]
],
"groups": [
{
"id": 1,
"title": "Save SRT",
"bounding": [
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],
"color": "#3f789e",
"font_size": 24,
"flags": {}
}
],
"config": {},
"extra": {
"ds": {
"scale": 0.5450529555861997,
"offset": [
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]
},
"frontendVersion": "1.35.9",
"workflowRendererVersion": "LG",
"VHS_latentpreview": false,
"VHS_latentpreviewrate": 0,
"VHS_MetadataImage": true,
"VHS_KeepIntermediate": true
},
"version": 0.4
}
================================================
FILE: pyproject.toml
================================================
[project]
name = "comfyui-whisper"
description = "Transcribe audio and add subtitles to videos using Whisper in ComfyUI"
version = "1.0.0"
license = {file = "LICENSE"}
dependencies = ["openai-whisper", "pillow", "uuid"]
[project.urls]
Repository = "https://github.com/yuvraj108c/ComfyUI-Whisper"
# Used by Comfy Registry https://comfyregistry.org
[tool.comfy]
PublisherId = "yuvraj108c"
DisplayName = "ComfyUI-Whisper"
Icon = ""
================================================
FILE: readme.md
================================================
# ComfyUI Whisper
Transcribe audio and add subtitles to videos using [Whisper](https://github.com/openai/whisper/) in [ComfyUI](https://github.com/comfyanonymous/ComfyUI).
Support multiple languages, prompt guidance and multiple whisper models.
**Last tested**: 2 January 2026 (ComfyUI v0.7.0@f2fda02 | Torch 2.9.1 | Triton 3.5.1 | Python 3.10.12 | RTX4090 | CUDA 13.0 | Debian 12)
## ⭐ Support
If you like my projects and wish to see updates and new features, please consider supporting me. It helps a lot!
[](https://github.com/yuvraj108c/ComfyUI-Depth-Anything-Tensorrt)
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[](https://github.com/yuvraj108c/ComfyUI-Rife-Tensorrt)
[](https://github.com/yuvraj108c/ComfyUI-Whisper)
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---
## Installation
Install via [ComfyUI Manager](https://github.com/ltdrdata/ComfyUI-Manager)
## Usage
Load this [workflow](https://github.com/yuvraj108c/ComfyUI-Whisper/blob/master/example_workflows/whisper_video_subtitles_workflow.json) into ComfyUI
Models are auto-downloaded to `/ComfyUI/models/stt/whisper`
## Supported Models
'tiny.en', 'tiny', 'base.en', 'base', 'small.en', 'small', 'medium.en', 'medium', 'large-v1', 'large-v2', 'large-v3', 'large', 'large-v3-turbo', 'turbo'
## Nodes
### Apply Whisper
Transcribe audio and get timestamps for each segment and word.
### Add Subtitles To Frames
Add subtitles on the video frames. You can specify font family, font color and x/y positions.
### Add Subtitles To Background (Experimental)
Add subtitles like wordcloud on blank frames
### Save SRT
Export alignments as SRT files in `/ComfyUI/output/srt` directory
## Updates
### 2 January 2026
- Export alignments as SRT
- Add `torchcodec` to requirements
### 27 August 2025
- Merge https://github.com/yuvraj108c/ComfyUI-Whisper/pull/22 by [@francislabountyjr](https://github.com/francislabountyjr) for model patcher, more whisper models support, comfyui model directory support
- Merge https://github.com/yuvraj108c/ComfyUI-Whisper/pull/18 by [@qy8502](https://github.com/qy8502) for Prompt Guidance support
- Support YRDZST Semibold Font
### 2 May 2025
- Merge https://github.com/yuvraj108c/ComfyUI-Whisper/pull/15 by [@niknah](https://github.com/niknah) for language selection
## Credits
- [comfyanonymous/ComfyUI](https://github.com/comfyanonymous/ComfyUI)
- [Kosinkadink/ComfyUI-VideoHelperSuite](https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite)
- [melMass/comfy_mtb](https://github.com/melMass/comfy_mtb)
## License
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
================================================
FILE: requirements.txt
================================================
openai-whisper
pillow
uuid
torchcodec
================================================
FILE: resize_cropped_subtitles.py
================================================
from .utils import tensor2pil, pil2tensor
from PIL import Image
class ResizeCroppedSubtitlesNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"cropped_subtitles": ("IMAGE",),
"original_frames": ("IMAGE",),
"subtitle_coord": ("subtitle_coord",),
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("IMAGE",)
FUNCTION = "resize_cropped_subtitles"
CATEGORY = "whisper"
def resize_cropped_subtitles(self,cropped_subtitles, original_frames, subtitle_coord):
pil_images_og = tensor2pil(original_frames)
pil_images_cropped = tensor2pil(cropped_subtitles)
final_images = []
print(len(cropped_subtitles), len(original_frames), len(subtitle_coord))
width, height = pil_images_og[0].size
for idx in range(len(pil_images_cropped)):
frame = Image.new("RGB", (width, height), "black")
frame.paste(pil_images_cropped[idx],(int(subtitle_coord[idx][0]),int(subtitle_coord[idx][1])))
final_images.append(frame)
return (pil2tensor(final_images),)
================================================
FILE: save_srt.py
================================================
# mostly generated using claude
import folder_paths
import json
import os
class SaveSRTNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"alignment" : ("whisper_alignment",),
"name": ("STRING",{"default": "subtitles"}),
}
}
RETURN_TYPES = ("STRING", )
RETURN_NAMES = ("srt_path",)
FUNCTION = "save_srt"
CATEGORY = "whisper"
def seconds_to_srt_time(self, seconds):
"""Convert seconds to SRT time format (HH:MM:SS,mmm)"""
hours = int(seconds // 3600)
minutes = int((seconds % 3600) // 60)
secs = int(seconds % 60)
milliseconds = int((seconds % 1) * 1000)
return f"{hours:02d}:{minutes:02d}:{secs:02d},{milliseconds:03d}"
def json_to_srt(self, json_data):
"""Convert JSON subtitle data to SRT format"""
# Parse JSON if it's a string
if isinstance(json_data, str):
data = json.loads(json_data)
else:
data = json_data
# Generate SRT content
srt_content = []
for i, entry in enumerate(data, start=1):
start_time = self.seconds_to_srt_time(entry['start'])
end_time = self.seconds_to_srt_time(entry['end'])
text = entry['value']
srt_content.append(f"{i}")
srt_content.append(f"{start_time} --> {end_time}")
srt_content.append(text)
srt_content.append("") # Empty line between entries
return srt_content
def save_srt(self, alignment, name):
subfolder = "srt"
output_dir = os.path.join(folder_paths.get_output_directory(), subfolder)
os.makedirs(output_dir,exist_ok=True)
srt_save_path = os.path.join(output_dir, name) + ".srt"
srt_content = self.json_to_srt(alignment)
with open(srt_save_path, 'w', encoding='utf-8') as f:
f.write('\n'.join(srt_content))
return (srt_save_path,)
================================================
FILE: utils.py
================================================
import torch
import numpy as np
from PIL import Image
# https://github.com/melMass/comfy_mtb/blob/501c3301056b2851555cccd75ab3ff15b1ab8e0c/utils.py#L261-L298
def tensor2pil(image):
batch_count = image.size(0) if len(image.shape) > 3 else 1
if batch_count > 1:
out = []
for i in range(batch_count):
out.extend(tensor2pil(image[i]))
return out
return [
Image.fromarray(
np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
)
]
def pil2tensor(image):
if isinstance(image, list):
return torch.cat([pil2tensor(img) for img in image], dim=0)
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
# https://github.com/comfyanonymous/ComfyUI/blob/fc196aac80fd~4bf6c8a39d85d1e809902871cade/comfy_extras/nodes_mask.py#L127
def tensor2Mask(image):
return image[:, :, :, 0]
gitextract_roxluuhh/ ├── .github/ │ └── workflows/ │ └── publish.yml ├── .gitignore ├── LICENSE ├── __init__.py ├── add_subtitles_to_background.py ├── add_subtitles_to_frames.py ├── apply_whisper.py ├── example_workflows/ │ └── whisper_video_subtitles_workflow.json ├── pyproject.toml ├── readme.md ├── requirements.txt ├── resize_cropped_subtitles.py ├── save_srt.py └── utils.py
SYMBOL INDEX (27 symbols across 6 files)
FILE: add_subtitles_to_background.py
class AddSubtitlesToBackgroundNode (line 9) | class AddSubtitlesToBackgroundNode:
method INPUT_TYPES (line 11) | def INPUT_TYPES(s):
method add_subtitles_to_background (line 57) | def add_subtitles_to_background(self, images, alignment, font_family, ...
FILE: add_subtitles_to_frames.py
class AddSubtitlesToFramesNode (line 8) | class AddSubtitlesToFramesNode:
method INPUT_TYPES (line 10) | def INPUT_TYPES(s):
method add_subtitles_to_frames (line 51) | def add_subtitles_to_frames(self, images, alignment, font_family, font...
FILE: apply_whisper.py
class WhisperModelWrapper (line 18) | class WhisperModelWrapper(torch.nn.Module):
method __init__ (line 24) | def __init__(self, model_name, download_root):
method load_model (line 31) | def load_model(self, device):
class WhisperPatcher (line 42) | class WhisperPatcher(comfy.model_patcher.ModelPatcher):
method __init__ (line 47) | def __init__(self, model, *args, **kwargs):
method patch_model (line 50) | def patch_model(self, device_to=None, *args, **kwargs):
method unpatch_model (line 67) | def unpatch_model(self, device_to=None, unpatch_weights=True, *args, *...
class ApplyWhisperNode (line 79) | class ApplyWhisperNode:
method INPUT_TYPES (line 83) | def INPUT_TYPES(s):
method apply_whisper (line 103) | def apply_whisper(self, audio, model, language, prompt):
FILE: resize_cropped_subtitles.py
class ResizeCroppedSubtitlesNode (line 4) | class ResizeCroppedSubtitlesNode:
method INPUT_TYPES (line 6) | def INPUT_TYPES(s):
method resize_cropped_subtitles (line 20) | def resize_cropped_subtitles(self,cropped_subtitles, original_frames, ...
FILE: save_srt.py
class SaveSRTNode (line 7) | class SaveSRTNode:
method INPUT_TYPES (line 9) | def INPUT_TYPES(s):
method seconds_to_srt_time (line 22) | def seconds_to_srt_time(self, seconds):
method json_to_srt (line 30) | def json_to_srt(self, json_data):
method save_srt (line 53) | def save_srt(self, alignment, name):
FILE: utils.py
function tensor2pil (line 6) | def tensor2pil(image):
function pil2tensor (line 20) | def pil2tensor(image):
function tensor2Mask (line 27) | def tensor2Mask(image):
Condensed preview — 14 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (70K chars).
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{
"path": ".github/workflows/publish.yml",
"chars": 565,
"preview": "name: Publish to Comfy registry\non:\n workflow_dispatch:\n push:\n branches:\n - main\n paths:\n - \"pyprojec"
},
{
"path": ".gitignore",
"chars": 21,
"preview": "__pycache__\n.DS_Store"
},
{
"path": "LICENSE",
"chars": 20845,
"preview": "Attribution-NonCommercial-ShareAlike 4.0 International\n\n================================================================"
},
{
"path": "__init__.py",
"chars": 907,
"preview": "from .apply_whisper import ApplyWhisperNode\nfrom .add_subtitles_to_frames import AddSubtitlesToFramesNode\nfrom .add_subt"
},
{
"path": "add_subtitles_to_background.py",
"chars": 7714,
"preview": "from PIL import ImageDraw, ImageFont, Image\nfrom .utils import pil2tensor,tensor2pil\nimport math\nimport os\nimport random"
},
{
"path": "add_subtitles_to_frames.py",
"chars": 6222,
"preview": "from PIL import ImageDraw, ImageFont, Image\nfrom .utils import tensor2pil, pil2tensor, tensor2Mask\nimport math\nimport os"
},
{
"path": "apply_whisper.py",
"chars": 6386,
"preview": "import whisper\nimport os\nimport folder_paths\nimport uuid\nimport torchaudio\nimport torch\nimport logging\n\nimport comfy.mod"
},
{
"path": "example_workflows/whisper_video_subtitles_workflow.json",
"chars": 14558,
"preview": "{\n \"id\": \"13ec9b05-13e3-4ce0-a274-dfe796b6c75d\",\n \"revision\": 0,\n \"last_node_id\": 98,\n \"last_link_id\": 212,\n \"nodes"
},
{
"path": "pyproject.toml",
"chars": 432,
"preview": "[project]\nname = \"comfyui-whisper\"\ndescription = \"Transcribe audio and add subtitles to videos using Whisper in ComfyUI\""
},
{
"path": "readme.md",
"chars": 4094,
"preview": "# ComfyUI Whisper\n\nTranscribe audio and add subtitles to videos using [Whisper](https://github.com/openai/whisper/) in ["
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"chars": 38,
"preview": "openai-whisper\npillow\nuuid\ntorchcodec\n"
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{
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"preview": "from .utils import tensor2pil, pil2tensor\nfrom PIL import Image\n\nclass ResizeCroppedSubtitlesNode:\n @classmethod\n "
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{
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"chars": 2022,
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},
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"preview": "import torch\nimport numpy as np\nfrom PIL import Image\n\n# https://github.com/melMass/comfy_mtb/blob/501c3301056b2851555cc"
}
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About this extraction
This page contains the full source code of the yuvraj108c/ComfyUI-Whisper GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 14 files (64.3 KB), approximately 16.0k tokens, and a symbol index with 27 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.