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# # import os
# # import streamlit as st
# # from pydub import AudioSegment
# # from groq import Groq
# # # Set ffmpeg path
# # ffmpeg_path = r"ffmpeg.exe"
# # os.environ["PATH"] += os.pathsep + os.path.dirname(ffmpeg_path)
# # AudioSegment.converter = ffmpeg_path
# # # Groq API configuration
# # groq_api_key = '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' # Replace with your actual API key
# # client = Groq(api_key=groq_api_key)
# # model = 'whisper-large-v3'
# # # Function to ensure the file is in a suitable format
# # def ensure_suitable_format(file_path):
# # allowed_formats = ["flac", "mp3", "mp4", "mpeg", "mpga", "m4a", "ogg", "opus", "wav", "webm"]
# # file_extension = file_path.split('.')[-1].lower()
# # if file_extension not in allowed_formats:
# # new_file_path = f"{os.path.splitext(file_path)[0]}.wav"
# # os.rename(file_path, new_file_path)
# # return new_file_path
# # return file_path
# # # Function to convert audio to WAV
# # def convert_audio_to_wav(input_path, output_path):
# # audio = AudioSegment.from_file(input_path)
# # audio.export(output_path, format="wav")
# # return output_path
# # # Function to transcribe audio using Groq
# # def audio_to_text(filepath):
# # with open(filepath, "rb") as file:
# # translation = client.audio.translations.create(
# # file=(filepath, file.read()),
# # model=model,
# # )
# # return translation.text
# # # Streamlit App UI
# # st.title("Audio-to-Text Transcription")
# # st.write("Upload an audio file to get the transcribed text.")
# # # File upload
# # uploaded_file = st.file_uploader("Upload your audio file", type=["flac", "mp3", "mp4", "mpeg", "mpga", "m4a", "ogg", "opus", "wav", "webm"])
# # if uploaded_file:
# # # Save the uploaded file locally
# # file_path = os.path.join("uploaded_audio", uploaded_file.name)
# # os.makedirs("uploaded_audio", exist_ok=True)
# # with open(file_path, "wb") as f:
# # f.write(uploaded_file.getbuffer())
# # st.write(f"File uploaded: {uploaded_file.name}")
# # # Ensure file format is suitable
# # suitable_audio_path = ensure_suitable_format(file_path)
# # # Convert audio to WAV
# # wav_path = f"{os.path.splitext(suitable_audio_path)[0]}.wav"
# # converted_audio = convert_audio_to_wav(suitable_audio_path, wav_path)
# # # Transcribe audio
# # st.write("Processing transcription...")
# # try:
# # transcription = audio_to_text(converted_audio)
# # st.success("Transcription complete!")
# # st.text_area("Transcribed Text", transcription, height=200)
# # except Exception as e:
# # st.error(f"Error during transcription: {e}")
# from pydub import AudioSegment
# from groq import Groq
# import os
# import streamlit as st
# # Groq API configuration
# groq_api_key = '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' # Replace with your actual API key
# client = Groq(api_key=groq_api_key)
# model = 'whisper-large-v3'
# # Function to ensure the file is in a suitable format
# def ensure_suitable_format(file_path):
# allowed_formats = ["flac", "mp3", "mp4", "mpeg", "mpga", "m4a", "ogg", "opus", "wav", "webm"]
# file_extension = file_path.split('.')[-1].lower()
# if file_extension not in allowed_formats:
# new_file_path = f"{os.path.splitext(file_path)[0]}.wav"
# os.rename(file_path, new_file_path)
# return new_file_path
# return file_path
# # Function to convert audio to WAV
# def convert_audio_to_wav(input_path, output_path):
# audio = AudioSegment.from_file(input_path)
# audio.export(output_path, format="wav")
# return output_path
# # Function to transcribe audio using Groq
# def audio_to_text(filepath):
# with open(filepath, "rb") as file:
# translation = client.audio.translations.create(
# file=(filepath, file.read()),
# model=model,
# )
# return translation.text
# # Streamlit App UI
# st.title("Audio-to-Text Transcription")
# st.write("Upload an audio file to get the transcribed text.")
# # File upload
# uploaded_file = st.file_uploader("Upload your audio file", type=["flac", "mp3", "mp4", "mpeg", "mpga", "m4a", "ogg", "opus", "wav", "webm"])
# if uploaded_file:
# # Save the uploaded file locally
# file_path = os.path.join("uploaded_audio", uploaded_file.name)
# os.makedirs("uploaded_audio", exist_ok=True)
# with open(file_path, "wb") as f:
# f.write(uploaded_file.getbuffer())
# st.write(f"File uploaded: {uploaded_file.name}")
# # Ensure file format is suitable
# suitable_audio_path = ensure_suitable_format(file_path)
# # Convert audio to WAV
# wav_path = f"{os.path.splitext(suitable_audio_path)[0]}.wav"
# converted_audio = convert_audio_to_wav(suitable_audio_path, wav_path)
# # Transcribe audio
# st.write("Processing transcription...")
# try:
# transcription = audio_to_text(converted_audio)
# st.success("Transcription complete!")
# st.text_area("Transcribed Text", transcription, height=200)
# except Exception as e:
# st.error(f"Error during transcription: {e}")
import os
import streamlit as st
from pydub import AudioSegment
from groq import Groq
# Function to ensure the file is in a suitable format
def ensure_suitable_format(file_path):
allowed_formats = ["flac", "mp3", "mp4", "mpeg", "mpga", "m4a", "ogg", "opus", "wav", "webm"]
file_extension = file_path.split('.')[-1].lower()
if file_extension not in allowed_formats:
new_file_path = f"{os.path.splitext(file_path)[0]}.wav"
os.rename(file_path, new_file_path)
return new_file_path
return file_path
# Function to convert audio to WAV
def convert_audio_to_wav(input_path, output_path):
audio = AudioSegment.from_file(input_path)
audio.export(output_path, format="wav")
return output_path
# Function to transcribe audio using Groq
def audio_to_text(filepath, groq_api_key):
client = Groq(api_key=groq_api_key)
model = 'whisper-large-v3'
with open(filepath, "rb") as file:
translation = client.audio.translations.create(
file=(filepath, file.read()),
model=model,
)
return translation.text
# Streamlit App UI
st.title("Audio-to-Text Transcription")
st.write("Upload an audio file to get the transcribed text.")
# Input for API key
groq_api_key = st.text_input("Enter your Groq API Key", type="password")
# File upload
uploaded_file = st.file_uploader("Upload your audio file", type=["flac", "mp3", "mp4", "mpeg", "mpga", "m4a", "ogg", "opus", "wav", "webm"])
if groq_api_key and uploaded_file:
# Save the uploaded file locally
file_path = os.path.join("uploaded_audio", uploaded_file.name)
os.makedirs("uploaded_audio", exist_ok=True)
with open(file_path, "wb") as f:
f.write(uploaded_file.getbuffer())
st.write(f"File uploaded: {uploaded_file.name}")
# Ensure file format is suitable
suitable_audio_path = ensure_suitable_format(file_path)
# Convert audio to WAV
wav_path = f"{os.path.splitext(suitable_audio_path)[0]}.wav"
converted_audio = convert_audio_to_wav(suitable_audio_path, wav_path)
# Transcribe audio
st.write("Processing transcription...")
try:
transcription = audio_to_text(converted_audio, groq_api_key)
st.success("Transcription complete!")
st.text_area("Transcribed Text", transcription, height=200)
except Exception as e:
st.error(f"Error during transcription: {e}")
elif not groq_api_key:
st.warning("Please enter your Groq API Key to proceed.")
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