app.py

import librosa
import gradio as gr
#from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC
from transformers import pipeline

#Loading the model and the tokenizer
model_name = "unilux/wav2vec-xls-r-Luxembourgish20-with-LM"
pipe = pipeline("automatic-speech-recognition", model=model_name)

#tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name)
#model = Wav2Vec2ForCTC.from_pretrained(model_name)


def load_data(input_file):
 
 """ Function for resampling to ensure that the speech input is sampled at 16KHz.
 """
 #read the file
 speech, sample_rate = librosa.load(input_file)
 #make it 1-D
 if len(speech.shape) > 1: 
 speech = speech[:,0] + speech[:,1]
 #Resampling at 16KHz since wav2vec2-base-960h is pretrained and fine-tuned on speech audio sampled at 16 KHz.
 if sample_rate !=16000:
 speech = librosa.resample(speech, sample_rate,16000)
 return speech
 
def asr_pipe(input_file):
 transcription = pipe(input_file, chunk_length_s=3, stride_length_s=(0.5, 0.5))
 return transcription
 

gr.Interface(asr_pipe,
 inputs = [
 gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Hei kënnt Dir Är Sprooch iwwert de Mikro ophuelen"),
 gr.inputs.Audio(source="upload", type='filepath', optional=True)
 ],
 outputs = gr.outputs.Textbox(label="Output Text"),
 title="Sproocherkennung fir d'Lëtzebuergescht @uni.lu",
 description = "Dës App convertéiert Är geschwate Sprooch an de (méi oder manner richegen ;-)) Text!",
 examples = [["ChamberMeisch.wav"], ["Chamber_Fayot_2005.wav"]], theme="default").launch()