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| import gradio as gr | |
| import numpy as np | |
| import librosa | |
| import requests | |
| import torch | |
| import torchaudio | |
| import math | |
| import os | |
| import soundfile as sf | |
| from glob import glob | |
| from pytube import YouTube | |
| from transformers import ( | |
| Wav2Vec2CTCTokenizer, | |
| Wav2Vec2FeatureExtractor, | |
| Wav2Vec2Processor, | |
| Wav2Vec2ForCTC, | |
| TrainingArguments, | |
| Trainer, | |
| pipeline | |
| ) | |
| processor = Wav2Vec2Processor.from_pretrained( | |
| "airesearch/wav2vec2-large-xlsr-53-th") | |
| model = Wav2Vec2ForCTC.from_pretrained( | |
| "BALAKA/wav2vec2-large-xlsr-53-th-swear-words") | |
| demo = gr.Blocks() | |
| def check(sentence): | |
| found = [] | |
| negative = ["กระดอ", "กระทิง", "กระสัน", "กระหรี่", "กรีด", "กวนส้นตีน", "กะหรี่", "กินขี้ปี้เยี่ยว", "ขายตัว", "ขี้", "ขโมย", "ข่มขืน", "ควย", "ควาย", "คอขาด", "ฆ่า", "ค่า", "จังไร", "จัญไร", "ฉิบหาย", "ฉี่", "ชั่ว", "ชาติหมา", "ชิงหมาเกิด", "ชิบหาย", "ช้างเย็ด", "ดาก", "ตอแหล", "ตัดหัว", "ตัดหำ", "ตาย", "ตีกัน", "ทรมาน", "ทาส", "ทุเรศ", "นรก", "บีบคอ", "ปากหมา", "ปี้กัน", "พ่อง", "พ่อมึง", "ฟัก", "ฟาย", "ยัดแม่", "ยิงกัน", "ระยำ", "ดอกทอง", "โสเภณี", "ล่อกัน", "ศพ", "สถุล", "สทุน", "สัด", "สันดาน", "สัส", "สาด", "ส้นตีน", "หน้าตัวเมืย", "ส้นตีน", "หมอย", "หรรม", "หัวแตก", "หำ", "หน้าหี", "น่าหี", "อนาจาร", "อัปปรี", "อีช้าง", "อีปลาวาฬ", "อีสัด", "อีหน้าหี", "อีหมา", "ห่า", "อับปรี", "เฆี่ยน", "เงี่ยน", "เจี๊ยว", "เชี่ย", "เด้า", "เผด็จการ", "เยี่ยว", "เย็ด", "เลือด", "เสือก", "เหล้า", "เหี้ย", "เอากัน", "แดก", "แตด", "แทง", "แม่ง", "แม่มึง", "แรด", "โคตร", "โง่", "โป๊", "โรคจิต", "ใจหมา", "ไอเข้", "ไอ้ขึ้หมา", "ไอ้บ้า", "ไอ้หมา", "เวร", "เวน", "ไอ้มืด", "ไอ้ดำ", "นิกก้า", "คนดำ", "นิโก", "บิช", "ดาก", "ปืน", "กระสุน", "โลลิ", ] | |
| negative = list(dict.fromkeys(negative)) | |
| for i in negative: | |
| if sentence.find(i) != -1: | |
| found.append(i) | |
| return found | |
| def resample(file_path): | |
| speech_array, sampling_rate = torchaudio.load(file_path) | |
| resampler = torchaudio.transforms.Resample(sampling_rate, 16000) | |
| return resampler(speech_array)[0].numpy() | |
| def tran_script(file_path): | |
| if isinstance(file_path, str): | |
| speech = resample(file_path) | |
| inputs = processor(speech, sampling_rate=16_000, | |
| return_tensors="pt", padding=True) | |
| logits = model(inputs.input_values).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| predicted_sentence = processor.batch_decode(predicted_ids) | |
| return predicted_sentence | |
| else: | |
| now_path = glob('/home/user/app/split_*.mp3') | |
| sentence = [] | |
| for i in range(file_path - 1): | |
| now_path = f'/home/user/app/split_{i+1}.mp3' | |
| speech = resample(now_path) | |
| inputs = processor(speech, sampling_rate=16_000, | |
| return_tensors="pt", padding=True) | |
| logits = model(inputs.input_values).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| predicted_sentence = processor.batch_decode(predicted_ids) | |
| sentence.append(predicted_sentence) | |
| return sentence | |
| def split_file(file_path): | |
| speech, sample_rate = librosa.load(file_path) | |
| buffer = 5 * sample_rate | |
| samples_total = len(speech) | |
| samples_wrote = 0 | |
| counter = 1 | |
| while samples_wrote < samples_total: | |
| if buffer > (samples_total - samples_wrote): | |
| buffer = samples_total - samples_wrote | |
| block = speech[samples_wrote: (samples_wrote + buffer)] | |
| out_filename = "split_" + str(counter) + ".mp3" | |
| sf.write(out_filename, block, sample_rate) | |
| counter += 1 | |
| samples_wrote += buffer | |
| return counter | |
| def process(file_path): | |
| if librosa.get_duration(filename=file_path) <= 5: | |
| sentence = tran_script(file_path) | |
| sentence = str(sentence).replace(' ', '').strip("[]") | |
| return 'found at 0.00m 0.00m 0.00-0.05 seconds found ' + str(check(sentence)) | |
| counter = split_file(file_path) | |
| sentence = tran_script(counter) | |
| result = '' | |
| for index, item in enumerate(sentence): | |
| now_sentence = item[0] | |
| now_sentence = str(item).replace(' ', '').strip("[]grt") | |
| now_sentence = check(now_sentence) | |
| if now_sentence: | |
| time = (index)*5 | |
| minutes = math.floor(time / 60) | |
| hours = math.floor(minutes/60) | |
| seconds = time % 60 | |
| minutes = str(minutes).zfill(2) | |
| hours = str(hours).zfill(2) | |
| fist_seconds = str(seconds).zfill(2) | |
| last_seconds = str(seconds+5).zfill(2) | |
| text = f'found at {hours}h {minutes}m {fist_seconds}-{last_seconds}seconds found {now_sentence}' | |
| result += text + '\n' | |
| return result | |
| def youtube_loader(link): | |
| yt = YouTube(str(link)) | |
| video = yt.streams.filter(only_audio=True).first() | |
| out_file = video.download(output_path='mp3') | |
| os.rename(out_file, '/home/user/app/mp3/youtube.mp3') | |
| return process('/home/user/app/mp3/youtube.mp3') | |
| def twitch_loader(link): | |
| os.system(f"twitch-dl download -q audio_only {link} --output twitch.wav") | |
| return process('/home/user/app/twitch.wav') | |
| with demo: | |
| gr.Markdown("Select your input type.") | |
| with gr.Tabs(): | |
| with gr.TabItem("From your voice."): | |
| with gr.Row(): | |
| voice = gr.Audio(source="microphone", type="filepath", | |
| optional=True, labe="Start record your voice here.") | |
| voice_output = gr.Textbox(labe="Your output is here.") | |
| text_button1 = gr.Button("Submit") | |
| with gr.TabItem("From your file."): | |
| with gr.Row(): | |
| file_input = gr.Audio( | |
| type="filepath", optional=True, labe="Drop your audio file here.") | |
| file_output = gr.Textbox(labe="Your output is here.") | |
| text_button4 = gr.Button("Submit") | |
| gr.Examples([["ex/ex1.mp3"], ["ex/ex2.mp3"]], | |
| inputs=file_input, outputs=file_output, fn=process) | |
| with gr.TabItem("From youtube"): | |
| with gr.Row(): | |
| youtube_input = gr.Textbox( | |
| label="Insert your youtube link here.", placeholder='https://www.youtube.com/watch?v=dQw4w9WgXcQ') | |
| youtube_output = gr.Textbox(labe="Your output is here.") | |
| text_button2 = gr.Button("Submit") | |
| gr.Examples([["https://youtu.be/JwOJWFniWS8"], ["https://youtu.be/B8TvZyoucxM"]], | |
| inputs=youtube_input, outputs=youtube_output, fn=youtube_loader) | |
| with gr.TabItem("From twitch"): | |
| with gr.Row(): | |
| twitch_input = gr.Textbox(label="Insert your twitch link or ID here.", | |
| placeholder='https://www.twitch.tv/videos/1823056925 or 1823056925') | |
| twitch_output = gr.Textbox(labe="Your output is here.") | |
| text_button3 = gr.Button("Submit") | |
| gr.Examples([["https://www.twitch.tv/videos/1823056925"], ["https://www.twitch.tv/videos/1827185416"]], | |
| inputs=twitch_input, outputs=twitch_output, fn=twitch_loader) | |
| text_button1.click(process, inputs=voice, outputs=voice_output) | |
| text_button2.click(youtube_loader, inputs=youtube_input, | |
| outputs=youtube_output) | |
| text_button3.click(twitch_loader, inputs=twitch_input, | |
| outputs=twitch_output) | |
| text_button4.click(process, inputs=file_input, | |
| outputs=file_output) | |
| demo.launch(enable_queue=True) | |