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import whisper
import streamlit as st
from streamlit_lottie import st_lottie
from utils import write_vtt, write_srt
import ffmpeg
import requests
from typing import Iterator
from io import StringIO
import numpy as np
import pathlib
import os
st.set_page_config(page_title="رونویس خودکار", page_icon="🔊", layout="wide")
# Define a function that we can use to load lottie files from a link.
@st.cache(allow_output_mutation=True)
def load_lottieurl(url: str):
r = requests.get(url)
if r.status_code != 200:
return None
return r.json()
APP_DIR = pathlib.Path(__file__).parent.absolute()
LOCAL_DIR = APP_DIR / "local_audio"
LOCAL_DIR.mkdir(exist_ok=True)
save_dir = LOCAL_DIR / "output"
save_dir.mkdir(exist_ok=True)
col1, col2 = st.columns([1, 3])
with col1:
lottie = load_lottieurl("https://assets1.lottiefiles.com/packages/lf20_1xbk4d2v.json")
st_lottie(lottie)
with col2:
st.write("""
## رونویس خودکار
##### یک فایل صوتی وارد کنید و رونوشت بگیرید.
###### ➠ اگر می خواهید صدا را به زبان اصلی آن رونویسی کنید، کار را به عنوان "Transcribe" انتخاب کنید.
###### ➠ اگر می خواهید رونویسی را به انگلیسی ترجمه کنید، کار را به عنوان "Translate" انتخاب کنید.
###### توصیه می کنم از مدل پایه شروع کنید و سپس با مدل های بزرگتر آزمایش کنید، مدل های کوچک و متوسط اغلب به خوبی کار می کنند. """)
loaded_model = whisper.load_model("base")
current_size = "None"
@st.cache(allow_output_mutation=True)
def change_model(current_size, size):
if current_size != size:
loaded_model = whisper.load_model(size)
return loaded_model
else:
raise Exception("Model size is the same as the current size.")
@st.cache(allow_output_mutation=True)
def inferecence(loaded_model, uploaded_file, task):
with open(f"{save_dir}/input.mp3", "wb") as f:
f.write(uploaded_file.read())
audio = ffmpeg.input(f"{save_dir}/input.mp3")
audio = ffmpeg.output(audio, f"{save_dir}/output.wav", acodec="pcm_s16le", ac=1, ar="16k")
ffmpeg.run(audio, overwrite_output=True)
if task == "Transcribe":
options = dict(task="transcribe", best_of=5)
results = loaded_model.transcribe(f"{save_dir}/output.wav", **options)
vtt = getSubs(results["segments"], "vtt", 80)
srt = getSubs(results["segments"], "srt", 80)
lang = results["language"]
return results["text"], vtt, srt, lang
elif task == "Translate":
options = dict(task="translate", best_of=5)
results = loaded_model.transcribe(f"{save_dir}/output.wav", **options)
vtt = getSubs(results["segments"], "vtt", 80)
srt = getSubs(results["segments"], "srt", 80)
lang = results["language"]
return results["text"], vtt, srt, lang
else:
raise ValueError("Task not supported")
def getSubs(segments: Iterator[dict], format: str, maxLineWidth: int) -> str:
segmentStream = StringIO()
if format == 'vtt':
write_vtt(segments, file=segmentStream, maxLineWidth=maxLineWidth)
elif format == 'srt':
write_srt(segments, file=segmentStream, maxLineWidth=maxLineWidth)
else:
raise Exception("Unknown format " + format)
segmentStream.seek(0)
return segmentStream.read()
def main():
size = st.selectbox("اندازه مدل را انتخاب کنید (هرچه مدل بزرگتر باشد، رونویسی دقیق تر خواهد بود، اما زمان بیشتری طول می کشد)", ["tiny", "base", "small", "medium", "large"], index=1)
loaded_model = change_model(current_size, size)
st.write(f"Model is {'multilingual' if loaded_model.is_multilingual else 'English-only'} "
f"and has {sum(np.prod(p.shape) for p in loaded_model.parameters()):,} parameters.")
input_file = st.file_uploader("Upload an audio file", type=["mp3", "wav", "m4a"])
if input_file is not None:
filename = input_file.name[:-4]
else:
filename = None
task = st.selectbox("Select Task", ["Transcribe", "Translate"], index=0)
if task == "Transcribe":
if st.button("Transcribe"):
results = inferecence(loaded_model, input_file, task)
col3, col4 = st.columns(2)
col5, col6, col7 = st.columns(3)
col9, col10 = st.columns(2)
with col3:
st.audio(input_file)
with open("transcript.txt", "w+", encoding='utf8') as f:
f.writelines(results[0])
f.close()
with open(os.path.join(os.getcwd(), "transcript.txt"), "rb") as f:
datatxt = f.read()
with open("transcript.vtt", "w+",encoding='utf8') as f:
f.writelines(results[1])
f.close()
with open(os.path.join(os.getcwd(), "transcript.vtt"), "rb") as f:
datavtt = f.read()
with open("transcript.srt", "w+",encoding='utf8') as f:
f.writelines(results[2])
f.close()
with open(os.path.join(os.getcwd(), "transcript.srt"), "rb") as f:
datasrt = f.read()
with col5:
st.download_button(label="Download Transcript (.txt)",
data=datatxt,
file_name="transcript.txt")
with col6:
st.download_button(label="Download Transcript (.vtt)",
data=datavtt,
file_name="transcript.vtt")
with col7:
st.download_button(label="Download Transcript (.srt)",
data=datasrt,
file_name="transcript.srt")
with col9:
st.success("You can download the transcript in .srt format, edit it (if you need to) and upload it to YouTube to create subtitles for your video.")
with col10:
st.info("Streamlit refreshes after the download button is clicked. The data is cached so you can download the transcript again without having to transcribe the video again.")
elif task == "Translate":
if st.button("Translate to English"):
results = inferecence(loaded_model, input_file, task)
col3, col4 = st.columns(2)
col5, col6, col7 = st.columns(3)
col9, col10 = st.columns(2)
with col3:
st.audio(input_file)
with open("transcript.txt", "w+", encoding='utf8') as f:
f.writelines(results[0])
f.close()
with open(os.path.join(os.getcwd(), "transcript.txt"), "rb") as f:
datatxt = f.read()
with open("transcript.vtt", "w+",encoding='utf8') as f:
f.writelines(results[1])
f.close()
with open(os.path.join(os.getcwd(), "transcript.vtt"), "rb") as f:
datavtt = f.read()
with open("transcript.srt", "w+",encoding='utf8') as f:
f.writelines(results[2])
f.close()
with open(os.path.join(os.getcwd(), "transcript.srt"), "rb") as f:
datasrt = f.read()
with col5:
st.download_button(label="Download Transcript (.txt)",
data=datatxt,
file_name="transcript.txt")
with col6:
st.download_button(label="Download Transcript (.vtt)",
data=datavtt,
file_name="transcript.vtt")
with col7:
st.download_button(label="Download Transcript (.srt)",
data=datasrt,
file_name="transcript.srt")
with col9:
st.success("You can download the transcript in .srt format, edit it (if you need to) and upload it to YouTube to create subtitles for your video.")
with col10:
st.info("Streamlit refreshes after the download button is clicked. The data is cached so you can download the transcript again without having to transcribe the video again.")
else:
st.error("Please select a task.")
if __name__ == "__main__":
main()
st.markdown("###### Made with :heart: by [@bugbounted](https://github.com/bugbounted)") |