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import streamlit as st
import torch
import torchaudio
from transformers import pipeline
from streamlit_mic_recorder import mic_recorder,speech_to_text
import numpy as np
option = st.selectbox("How do you want to import the audio file?",("Microphone","Upload file"))
if option == "Microphone":
# Load your own audio file
st.write("Record your voice, and play the recorded audio:")
audio = mic_recorder(start_prompt="Press the botton to start recording ⏺️",stop_prompt="Press the botton to stop to stop the recording⏹️",key='recorder')
if audio == None:
st.write("Please start the recording in the box above")
else:
st.audio(audio["bytes"])
audio = audio['bytes']
elif option == "Upload file":
audio = st.file_uploader(label="Upload your audio file here",type=["wav",'mp3'])
if audio:
st.audio(audio)
option_language = st.selectbox(
'Select the language of your audio',
('English', 'Spanish', 'German','French','Chinese'))
if audio == None:
st.write("Please upload the audio in the box above")
else:
if option_language == "English":
def transcribe_audio(audio_file):
# Load the audio file
waveform, sample_rate = torchaudio.load(audio_file)
# Ensure mono-channel audio
if waveform.shape[0] > 1:
waveform = torch.mean(waveform, dim=0, keepdim=True)
# Convert to a 16kHz sample rate if not already
if sample_rate != 16000:
waveform = torchaudio.transforms.Resample(sample_rate, 16000)(waveform)
# Convert to a list of integers
audio_input = waveform.squeeze().numpy().astype(int).tolist()
# Use Hugging Face's ASR pipeline
asr_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-large-v2")
# Transcribe the audio
transcript = asr_pipeline(waveform.numpy()[0])
return transcript
transcription = transcribe_audio(audio)
st.write("Here is your transcription:")
st.write(transcription)
elif option_language == 'Spanish':
def transcribe_audio(audio_file):
# Load the audio file
waveform, sample_rate = torchaudio.load(audio_file)
# Ensure mono-channel audio
if waveform.shape[0] > 1:
waveform = torch.mean(waveform, dim=0, keepdim=True)
# Convert to a 16kHz sample rate if not already
if sample_rate != 16000:
waveform = torchaudio.transforms.Resample(sample_rate, 16000)(waveform)
# Convert to a list of integers
audio_input = waveform.squeeze().numpy().astype(int).tolist()
# Use Hugging Face's ASR pipeline
asr_pipeline = pipeline("automatic-speech-recognition", model="Sandiago21/whisper-large-v2-spanish")
# Transcribe the audio
transcript = asr_pipeline(waveform.numpy()[0])
return transcript
transcription = transcribe_audio(audio)
st.write("Aqui tienes tu transcripcion:")
st.write(transcription)
elif option_language == 'German':
def transcribe_audio(audio_file):
# Load the audio file
waveform, sample_rate = torchaudio.load(audio_file)
# Ensure mono-channel audio
if waveform.shape[0] > 1:
waveform = torch.mean(waveform, dim=0, keepdim=True)
# Convert to a 16kHz sample rate if not already
if sample_rate != 16000:
waveform = torchaudio.transforms.Resample(sample_rate, 16000)(waveform)
# Convert to a list of integers
audio_input = waveform.squeeze().numpy().astype(int).tolist()
# Use Hugging Face's ASR pipeline
asr_pipeline = pipeline("automatic-speech-recognition", model="primeline/whisper-large-v3-german")
# Transcribe the audio
transcript = asr_pipeline(waveform.numpy()[0])
return transcript
transcription = transcribe_audio(audio)
st.write("Hier ist Ihre Transkription:")
st.write(transcription)
elif option_language == "French":
def transcribe_audio(audio_file):
# Load the audio file
waveform, sample_rate = torchaudio.load(audio_file)
# Ensure mono-channel audio
if waveform.shape[0] > 1:
waveform = torch.mean(waveform, dim=0, keepdim=True)
# Convert to a 16kHz sample rate if not already
if sample_rate != 16000:
waveform = torchaudio.transforms.Resample(sample_rate, 16000)(waveform)
# Convert to a list of integers
audio_input = waveform.squeeze().numpy().astype(int).tolist()
# Use Hugging Face's ASR pipeline
asr_pipeline = pipeline("automatic-speech-recognition", model="bofenghuang/whisper-large-v2-french")
# Transcribe the audio
transcript = asr_pipeline(waveform.numpy()[0])
return transcript
transcription = transcribe_audio(audio)
st.write("Ici, vous avez votre transcription")
st.write(transcription)
elif option_language == "Chinese":
def transcribe_audio(audio_file):
# Load the audio file
waveform, sample_rate = torchaudio.load(audio_file)
# Ensure mono-channel audio
if waveform.shape[0] > 1:
waveform = torch.mean(waveform, dim=0, keepdim=True)
# Convert to a 16kHz sample rate if not already
if sample_rate != 16000:
waveform = torchaudio.transforms.Resample(sample_rate, 16000)(waveform)
# Convert to a list of integers
audio_input = waveform.squeeze().numpy().astype(int).tolist()
# Use Hugging Face's ASR pipeline
asr_pipeline = pipeline("automatic-speech-recognition", model="yi-ching/whisper-tiny-chinese-test")
# Transcribe the audio
transcript = asr_pipeline(waveform.numpy()[0])
return transcript
transcription = transcribe_audio(audio)
st.write("这是您的转录。")
st.write(transcription)
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