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import streamlit as st
from transformers import pipeline
from torch import cuda
import torchaudio
import torchaudio.functional as F
from pydub import AudioSegment
import logging
import io
class ASR:
def __init__(self):
self.model_name = "viktor-enzell/wav2vec2-large-voxrex-swedish-4gram"
self.device = cuda.current_device() if cuda.is_available() else -1
self.model = None
def load_model(self):
self.model = pipeline(model=self.model_name, device=self.device)
def run_inference(self, file):
audio = self.load_16khz_audio(file)
return self.model(audio, chunk_length_s=10)["text"].lower()
@staticmethod
def load_16khz_audio(file):
waveform, sample_rate = torchaudio.load(file)
if sample_rate == 16_000:
waveform = waveform[0]
else:
waveform = F.resample(waveform, sample_rate, 16_000)[0]
return waveform.numpy()
@st.cache(allow_output_mutation=True, show_spinner=False)
def load_model():
asr = ASR()
asr.load_model()
return asr
@st.cache(allow_output_mutation=True, hash_funcs={ASR: lambda _: None}, show_spinner=False)
def run_inference(asr, file):
return asr.run_inference(file)
def convert_uploaded_file_to_wav(file):
try:
media_type = file.type.split("/")[0]
file_extension = file.name.split(".")[-1]
if media_type != "audio" and media_type != "video":
return None
if file_extension == "wav":
return file
audio = AudioSegment.from_file(file, file_extension)
in_memory_buffer = io.BytesIO()
return audio.export(in_memory_buffer, format="wav")
except Exception as e:
logging.exception(e)
return None
if __name__ == "__main__":
st.set_page_config(
page_title="Swedish Speech-to-Text",
page_icon="🎙️"
)
st.image(
"https://emojipedia-us.s3.dualstack.us-west-1.amazonaws.com/thumbs/320/apple/325/studio-microphone_1f399-fe0f.png",
width=100,
)
st.markdown("""
# Swedish Speech-to-text
Generate and download high-quality Swedish transcripts for your audio and video files. The speech-to-text model is KBLab's wav2vec 2.0 large VoxRex Swedish (C) with a 4-gram language model, which you can access [here](https://huggingface.co/viktor-enzell/wav2vec2-large-voxrex-swedish-4gram).
""")
with st.spinner(text="Loading model..."):
asr = load_model()
uploaded_file = st.file_uploader("Choose a file")
if uploaded_file is not None:
file = convert_uploaded_file_to_wav(uploaded_file)
if file is None:
st.error(
"There was a problem handling the uploaded file. Try again using an audio or video file.")
else:
with st.spinner(text="Transcribing..."):
transcript = run_inference(asr, file)
st.download_button("Download transcript",
transcript, "transcript.txt")
with st.expander("Transcript", expanded=True):
st.write(transcript)
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