Create new file

app.py

@@ -0,0 +1,42 @@
+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()
+