Post your JAV subtitle files here - JAV Subtitle Repository (JSP)★NOT A SUB REQUEST THREAD★

[ericf]

I'm afraid I don't understand the text about moving parts of the script in collab.
The script is divided into
#@markdown **Run Whisper**
# @markdown Required settings:
# Generate VAD timestamps
# Add a bit of padding, and remove small gaps
# If breaks are longer than chunk_threshold seconds, split into a new audio file
# This'll effectively turn long transcriptions into many shorter ones
# Merge speech chunks
# Convert timestamps to seconds
# Run Whisper on each audio chunk
Lots of small edits to the above
# DeepL translation

# Write SRT file

I don't understand which 'block' to move and where.
Is it everything in the first block like this:

if "http://" in audio_path or "https://" in audio_path:
print("Downloading audio...")
urllib.request.urlretrieve(audio_path, "input_file")
audio_path = "input_file"
else:
if not os.path.exists(audio_path):
try:
audio_path = uploaded_file
if not os.path.exists(audio_path):
raise ValueError("Input audio not found. Is your audio_path correct?")
except NameError:
raise ValueError("Input audio not found. Did you upload a file?")
out_path = os.path.splitext(audio_path)[0] + ".srt"
out_path_pre = os.path.splitext(audio_path)[0] + "_Untranslated.srt"
if source_separation:
print("Separating vocals...")
!ffprobe -i "{audio_path}" -show_entries format=duration -v quiet -of csv="p=0" > input_length
with open("input_length") as f:
input_length = int(float(f.read())) + 1
!spleeter separate -d {input_length} -p spleeter:2stems -o output "{audio_path}"
spleeter_dir = os.path.basename(os.path.splitext(audio_path)[0])
audio_path = "output/" + spleeter_dir + "/vocals.wav"
print("Encoding audio...")
if not os.path.exists("vad_chunks"):
os.mkdir("vad_chunks")
ffmpeg.input(audio_path).output(
"vad_chunks/silero_temp.wav",
ar="16000",
ac="1",
acodec="pcm_s16le",
map_metadata="-1",
fflags="+bitexact",
).overwrite_output().run(quiet=True)
print("Running VAD...")
model, utils = torch.hub.load(
repo_or_dir="snakers4/silero-vad:v4.0", model="silero_vad", onnx=False
)
(get_speech_timestamps, save_audio, read_audio, VADIterator, collect_chunks) = utils

and move it to under
# Write SRT file

??

 

[ericf]

Whisper will convert the audio to that internally so it doesn't change anything, it's just ideal to do that if you modify the audio since it's a lossless format so you avoid degrading the audio with an extra lossy conversion.

There was a version issue after something updated on the original colab everyone was using here so one that forces the proper versions was made: https://colab.research.google.com/g...AV/blob/main/notebook/WhisperWithVAD_mr.ipynb

Not sure which you're using but that might change something if it's not that one.

How many lines it detects is a pretty bad indicator since medium might simply split up lines more or something like that or it might just makes a ton of mistakes, but I assume you took a closer look to them when comparing.

Yeah. It's a difference in file size: 16 kb vs 69 or 70 kb.
The settings for line division are off in Faster Whisper, some lines are very long (12 seconds) but I guess I can edit that later.

 

[SamKook]

You copy everything from the first step code, which is this if you keep the default:

#@title Whisper Transcription Parameters

model_size = "large-v2"  # @param ["large-v3", "large-v2", "medium", "large"]
language = "japanese"  # @param {type:"string"}
translation_mode = "End-to-end Whisper (default)"  # @param ["End-to-end Whisper (default)", "Whisper -> DeepL", "No translation"]
# @markdown VAD settings and DeepL:
deepl_authkey = ""  # @param {type:"string"}
source_separation = False  # @param {type:"boolean"}
vad_threshold = 0.4  # @param {type:"number"}
chunk_threshold = 3.0  # @param {type:"number"}
deepl_target_lang = "EN-US"  # @param {type:"string"}
max_attempts = 1  # @param {type:"integer"}

#@markdown Enter the values for the transcriber parameters. Leave unchanged if not sure.
verbose = False #@param {type:"boolean"}
temperature_input = "0.0" #@param {type:"string"}
compression_ratio_threshold = 2.4 #@param {type:"number"}
logprob_threshold = -1.0 #@param {type:"number"}
no_speech_threshold = 0.6 #@param {type:"number"}
condition_on_previous_text = False #@param {type:"boolean"}
initial_prompt = "" #@param {type:"string"}
word_timestamps = True #@param {type:"boolean"}
clip_timestamps_input = "0" #@param {type:"string"}
hallucination_silence_threshold = 2.0 #@param {type:"number"}

#@markdown Decoding Options (for advanced configurations, leave unchnaged if unsure):
best_of = 2 #@param {type:"number"}
beam_size = 2 #@param {type:"number"}
patience = 1 #@param {type:"number"}
length_penalty = "" #@param {type:"string"}
prefix = "" #@param {type:"string"}
suppress_tokens = "-1" #@param {type:"string"}
suppress_blank = True #@param {type:"boolean"}
without_timestamps = False #@param {type:"boolean"}
max_initial_timestamp = 1.0 #@param {type:"number"}
fp16 = True #@param {type:"boolean"}
# Parsing and converting form inputs
try:
    temperature = tuple(float(temp.strip()) for temp in temperature_input.split(',')) if ',' in temperature_input else float(temperature_input)
except ValueError:
    temperature = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0)  # Default
clip_timestamps = clip_timestamps_input.split(',') if ',' in clip_timestamps_input else clip_timestamps_input
if clip_timestamps != "0":
    try:
        clip_timestamps = list(map(float, clip_timestamps)) if isinstance(clip_timestamps, list) else float(clip_timestamps)
    except ValueError:
        clip_timestamps = "0"  # Default if parsing fails
language = None if not language else language
initial_prompt = None if initial_prompt == "" else initial_prompt
length_penalty = None if length_penalty == "" else float(length_penalty)

assert max_attempts >= 1
assert vad_threshold >= 0.01
assert chunk_threshold >= 0.1
assert language != ""
if translation_mode == "End-to-end Whisper (default)":
    task = "translate"
    run_deepl = False
elif translation_mode == "Whisper -> DeepL":
    task = "transcribe"
    run_deepl = True
elif translation_mode == "No translation":
    task = "transcribe"
    run_deepl = False
else:
    raise ValueError("Invalid translation mode")


# Prepare transcription options
transcription_options = {
    "verbose": verbose,
    "compression_ratio_threshold": compression_ratio_threshold,
    "logprob_threshold": logprob_threshold,
    "no_speech_threshold": no_speech_threshold,
    "condition_on_previous_text": condition_on_previous_text,
    "initial_prompt": initial_prompt,
    "word_timestamps": word_timestamps,
    "clip_timestamps": clip_timestamps,
    "hallucination_silence_threshold": hallucination_silence_threshold
}
# Prepare decoding options
decoding_options = {
    "task": task,
    "language": language,
    "temperature": temperature,
    "best_of": best_of,
    "beam_size": beam_size,
    "patience": patience,
    "length_penalty": length_penalty,
    "prefix": prefix,
    "suppress_tokens": suppress_tokens,
    "suppress_blank": suppress_blank,
    "without_timestamps": without_timestamps,
    "max_initial_timestamp": max_initial_timestamp,
    "fp16": fp16,
}

And then put it at the start of the last step code block which is:

#@markdown **Run Whisper**
# @markdown Required settings:
audio_path = "/content/drive/MyDrive/test.wav"  # @param {type:"string"}
assert audio_path != ""


import tensorflow as tf
import torch
import whisper
import os
import ffmpeg
import srt
from tqdm import tqdm
import datetime
import deepl
import urllib.request
import json
from google.colab import files

if "http://" in audio_path or "https://" in audio_path:
    print("Downloading audio...")
    urllib.request.urlretrieve(audio_path, "input_file")
    audio_path = "input_file"

*rest of the code here*

So you end up with:

#@title Whisper Transcription Parameters

model_size = "large-v2"  # @param ["large-v3", "large-v2", "medium", "large"]
language = "japanese"  # @param {type:"string"}
translation_mode = "End-to-end Whisper (default)"  # @param ["End-to-end Whisper (default)", "Whisper -> DeepL", "No translation"]
# @markdown VAD settings and DeepL:
deepl_authkey = ""  # @param {type:"string"}
source_separation = False  # @param {type:"boolean"}
vad_threshold = 0.4  # @param {type:"number"}
chunk_threshold = 3.0  # @param {type:"number"}
deepl_target_lang = "EN-US"  # @param {type:"string"}
max_attempts = 1  # @param {type:"integer"}

#@markdown Enter the values for the transcriber parameters. Leave unchanged if not sure.
verbose = False #@param {type:"boolean"}
temperature_input = "0.0" #@param {type:"string"}
compression_ratio_threshold = 2.4 #@param {type:"number"}
logprob_threshold = -1.0 #@param {type:"number"}
no_speech_threshold = 0.6 #@param {type:"number"}
condition_on_previous_text = False #@param {type:"boolean"}
initial_prompt = "" #@param {type:"string"}
word_timestamps = True #@param {type:"boolean"}
clip_timestamps_input = "0" #@param {type:"string"}
hallucination_silence_threshold = 2.0 #@param {type:"number"}

#@markdown Decoding Options (for advanced configurations, leave unchnaged if unsure):
best_of = 2 #@param {type:"number"}
beam_size = 2 #@param {type:"number"}
patience = 1 #@param {type:"number"}
length_penalty = "" #@param {type:"string"}
prefix = "" #@param {type:"string"}
suppress_tokens = "-1" #@param {type:"string"}
suppress_blank = True #@param {type:"boolean"}
without_timestamps = False #@param {type:"boolean"}
max_initial_timestamp = 1.0 #@param {type:"number"}
fp16 = True #@param {type:"boolean"}
# Parsing and converting form inputs
try:
    temperature = tuple(float(temp.strip()) for temp in temperature_input.split(',')) if ',' in temperature_input else float(temperature_input)
except ValueError:
    temperature = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0)  # Default
clip_timestamps = clip_timestamps_input.split(',') if ',' in clip_timestamps_input else clip_timestamps_input
if clip_timestamps != "0":
    try:
        clip_timestamps = list(map(float, clip_timestamps)) if isinstance(clip_timestamps, list) else float(clip_timestamps)
    except ValueError:
        clip_timestamps = "0"  # Default if parsing fails
language = None if not language else language
initial_prompt = None if initial_prompt == "" else initial_prompt
length_penalty = None if length_penalty == "" else float(length_penalty)

assert max_attempts >= 1
assert vad_threshold >= 0.01
assert chunk_threshold >= 0.1
assert language != ""
if translation_mode == "End-to-end Whisper (default)":
    task = "translate"
    run_deepl = False
elif translation_mode == "Whisper -> DeepL":
    task = "transcribe"
    run_deepl = True
elif translation_mode == "No translation":
    task = "transcribe"
    run_deepl = False
else:
    raise ValueError("Invalid translation mode")


# Prepare transcription options
transcription_options = {
    "verbose": verbose,
    "compression_ratio_threshold": compression_ratio_threshold,
    "logprob_threshold": logprob_threshold,
    "no_speech_threshold": no_speech_threshold,
    "condition_on_previous_text": condition_on_previous_text,
    "initial_prompt": initial_prompt,
    "word_timestamps": word_timestamps,
    "clip_timestamps": clip_timestamps,
    "hallucination_silence_threshold": hallucination_silence_threshold
}
# Prepare decoding options
decoding_options = {
    "task": task,
    "language": language,
    "temperature": temperature,
    "best_of": best_of,
    "beam_size": beam_size,
    "patience": patience,
    "length_penalty": length_penalty,
    "prefix": prefix,
    "suppress_tokens": suppress_tokens,
    "suppress_blank": suppress_blank,
    "without_timestamps": without_timestamps,
    "max_initial_timestamp": max_initial_timestamp,
    "fp16": fp16,
}


#@markdown **Run Whisper**
# @markdown Required settings:
audio_path = "/content/drive/MyDrive/test.wav"  # @param {type:"string"}
assert audio_path != ""


import tensorflow as tf
import torch
import whisper
import os
import ffmpeg
import srt
from tqdm import tqdm
import datetime
import deepl
import urllib.request
import json
from google.colab import files

if "http://" in audio_path or "https://" in audio_path:
    print("Downloading audio...")
    urllib.request.urlretrieve(audio_path, "input_file")
    audio_path = "input_file"

*rest of the code here*

 

[ericf]

Thanks. But no, that's not how the default script on that address looked from what I remember.
https://colab.research.google.com/g...hisperWithVAD_pro.ipynb#scrollTo=sos9vsxPkIN7
I'll check back when I've finished what I mention below.
I'll try that script later.

The new/old collab link you sent me seems to work. It does take a lot more time than the old one, though. It took forever before it started checking the audio and dividing it (in 375 chunks). It's at 81% after 30 minutes. Well, it's a 3 hr video.

Thank you so much for your time. I won't hold you up any longer. Have a nice day.

 

[porgate55555]

Thanks. But no, that's not how the default script on that address looked from what I remember.
https://colab.research.google.com/g...hisperWithVAD_pro.ipynb#scrollTo=sos9vsxPkIN7
I'll check back when I've finished what I mention below.
I'll try that script later.

The new/old collab link you sent me seems to work. It does take a lot more time than the old one, though. It took forever before it started checking the audio and dividing it (in 375 chunks). It's at 81% after 30 minutes. Well, it's a 3 hr video.

Thank you so much for your time. I won't hold you up any longer. Have a nice day.

I downloaded my adapted script. Just upload it to your colab. Works slightly different. Make sure to change this line in the code to the path where you store the audio: audio_folder = "/content/drive/MyDrive/Colab Notebooks/"
It will pick the audio from there and run it.

 

[SamKook]

Thanks. But no, that's not how the default script on that address looked from what I remember.
https://colab.research.google.com/g...hisperWithVAD_pro.ipynb#scrollTo=sos9vsxPkIN7

That is the link I copy pasted the code from so it should be identical, the first block(Whisper Transcription Parameters) code is hidden by default so you have to make it show up first.

 

[Chuckie100]

[Reducing Mosaic]JUR-007 Every Night, I Heard The Moans Of The Woman Next Door, And I Was Worried About It… ~A Sweaty Afternoon With A Frustrated Married Woman~ Rena Fukiishi​

I downloaded this recent, reduced mosaic JAV starring one of my favorite MILFs. I used Purfview's Whisper in Subtitle Edit (using model V2-Large) to create this Sub. I also attempted to clean it up a bit and re-interpreted some of the meaningless/ "lewd-less" dialog. Again, I don't understand Japanese so my re-interpretations might not be totally accurate but I try to match what's happening in the scene. Anyway, enjoy and let me know what you think.​

Not much of a storyline nor is it an Incest themed movie but It is Rena Fukiishi!​

 

[ericf]

That is the link I copy pasted the code from so it should be identical, the first block(Whisper Transcription Parameters) code is hidden by default so you have to make it show up first.

Ahh! I see now. I was looking at the parameters at Run Whisper only.

So I put all that code before this?:

else:
if not os.path.exists(audio_path):
try:
audio_path = uploaded_file
if not os.path.exists(audio_path):
raise ValueError("Input audio not found. Is your audio_path correct?")
except NameError:
raise ValueError("Input audio not found. Did you upload a file?")

Or do I remove the else: part since it refers back to
if "http://" in audio_path or "https://" in audio_path:
?
You see how little I know about scripts?
Thanks for the information.

 

[SamKook]

Remove nothing, just put the whole thing at the beginning.

It's just variable declarations so it just needs to be there and not overlap with another line.

There might be a better way to use the colab, but that's just my solution for not having looked into the issue more than a few secs.

 

[Chuckie100]

[Reducing Mosaic]MEYD-090 Was Being × Up To Father-in-law While You Are Not … Ayumi Shinoda​

I downloaded this recent, reduced mosaic JAV starring my favoritest MILF. I used Purfview's Whisper in Subtitle Edit (using model V2-Large) to create this Sub. I also attempted to clean it up a bit and re-interpreted some of the meaningless/ "lewd-less" dialog. Again, I don't understand Japanese so my re-interpretations might not be totally accurate but I try to match what's happening in the scene. Anyway, enjoy and let me know what you think.​

Not my preferred flavor of Incest JAV, but it was Ayumi Shinoda and it was pretty erotic.​

 

[Chuckie100]

[Reducing Mosaic]JFB-011 Four Hours I’m The Only Mom Of Six-friendly (Sons)​

I downloaded this recent, reduced mosaic JAV starring many of my favorite MILFs. I used Purfview's Whisper in Subtitle Edit (using model V2-Large) to create this Sub. I also attempted to clean it up a bit and re-interpreted some of the meaningless/ "lewd-less" dialog. Again, I don't understand Japanese so my re-interpretations might not be totally accurate but I try to match what's happening in the scene. Anyway, enjoy and let me know what you think.​

 

[asianbooblover]

You 'da man!

 

[mei2]

Could you help me with the error message I get?
NameError Traceback (most recent call last)

<ipython-input-6-1bc508f8c32b> in <cell line: 37>()
35 out_path = os.path.splitext(audio_path)[0] + ".srt"
36 out_path_pre = os.path.splitext(audio_path)[0] + "_Untranslated.srt"
---> 37 if source_separation:
38 print("Separating vocals...")
39 get_ipython().system('ffprobe -i "{audio_path}" -show_entries format=duration -v quiet -of csv="p=0" > input_length')


NameError: name 'source_separation' is not defined

The help in Whisper doesn't seem to make it clear what it is I'm supposed to do.
I have tried to just check the box at Source Separation but that doesn't do it.
Thanks.

This is late reply and I see that you have found various workarounds already.
In case this happens again in future:
- Keep source-separation unchecked. Source-separation extracts vocals from the music in the audio by using spleeter. You'd only needed it in rare oxxasions that the background music overpowers the dialog.
- I believe the error happens if somehow the code execution has been interrupted and cell 1 of the notbook needs to be re-executed. Just press the (thick) arrow head by the first cell to get a green tickmark.
- I checked the code, it looks correct and should work. If you still see a problem please send me a screenshot of the notebook.

cheers

 

[Chuckie100]

[Reducing Mosaic]GGP-005 Christmas Creampie Sex Party With 3 Super Horny Office Ladies And A Dick That Gets Hard Again Immediately After Cumming!! 3 Consecutive Creampies In A Harem Heaven!!​

I couldn't resist downloading this recent, reduced mosaic Christmas themed JAV. I used Purfview's Whisper in Subtitle Edit (using model V2-Large) to create this Sub. I have to admit that I didn't spend much time to to clean it up though. Again, I don't understand Japanese so my re-interpretations might not be totally accurate but I try to match what's happening in the scene. Anyway, enjoy and let me know what you think.​

Wishing everyone a Very Merry Christmas and a Healthy and Prosperous New Year.​

 

[Imscully]

@Chuckie100
Don't you just love women who want to help you celebrate the holiday season by serving up their boobies, butts, and boxes?
Well...in the spirit of the season, I say we bring some special gifts of our own to that fantasy office party.

Ladies....our gifts will give a whole new meaning to this year's "White Elephant" gift exchange.
(but one caveat....they can only be swapped/traded/shared with other female staffers. These gifts are for ladies only!!!!)

 

[Fire117]

100% accurate English subtitles? That probably leaves most of us out!

Someone should be able to do it on here.

 

[ericf]

This is late reply and I see that you have found various workarounds already.
In case this happens again in future:
- Keep source-separation unchecked. Source-separation extracts vocals from the music in the audio by using spleeter. You'd only needed it in rare oxxasions that the background music overpowers the dialog.
- I believe the error happens if somehow the code execution has been interrupted and cell 1 of the notbook needs to be re-executed. Just press the (thick) arrow head by the first cell to get a green tickmark.
- I checked the code, it looks correct and should work. If you still see a problem please send me a screenshot of the notebook.

cheers

Thanks for replying.
I tried inserting the code from the first part of the script as instructed by SamKook but I get the same message at the end:



I don't use the locally installed files when using collab so why is this install problem happening?
I do get a red mark at the side of the script at the line:
import torch
Is this what you mean by Notebook? If it's the whole thing on that link:

Google Colab

colab.research.google.com

then I can't replicate that here. I only change the translation_mode to No translation. I keep the other settings as is.
I have uploaded the audio through a code that links to my Google Drive so it's stored in Whisper. Path is /content/***.wav. I don't check source_separation because I've always gotten a failure message when doing that.

Also, @porgate55555 how do I upload the script you posted to Whisper? I don't understand where I can load it.

 

[mei2]

Thanks for replying.
I tried inserting the code from the first part of the script as instructed by SamKook but I get the same message at the end:

If you could send me a screenshot that shows the (printouts of the) following parts of the colab, that would help.
And yes, colab is like a linux pc, you'd need to install all necessary libraries which are not part of the core colab/linux package.

 

[Chuckie100]

CEMD-613 My StepMother Seduced Me With Sex Every Day And Helped Me Recover From My Isolation 5 Nina Nishimura​

I saw this JAV and couldn't resist subbing it for my ol' friend Imscully. I used Purfview's Whisper in Subtitle Edit (using model V2-Large) to create this Sub. I have to admit that the orginal Sub didn't give me a lot to work with, but I tried to clean it up the best I could. Again, I don't understand Japanese so my re-interpretations might not be totally accurate but I try to match what's happening in the scene. Anyway, enjoy and let me know what you think.​

 

[Imscully]

CEMD-613 My StepMother Seduced Me With Sex Every Day And Helped Me Recover From My Isolation 5 Nina Nishimura​

I saw this JAV and couldn't resist subbing it for my ol' friend Imscully. I used Purfview's Whisper in Subtitle Edit (using model V2-Large) to create this Sub. I have to admit that the orginal Sub didn't give me a lot to work with, but I tried to clean it up the best I could. Again, I don't understand Japanese so my re-interpretations might not be totally accurate but I try to match what's happening in the scene. Anyway, enjoy and let me know what you think.​

Thanks, bro!! I added the laugh emoji because....as soon as I downloaded the movie, my first thought was "Dang, I can't wait for Chuckie to make subs for this!" Thanks for thinking of me!!!!