Create app.py

app.py

@@ -0,0 +1,75 @@
+import gradio as gr
+from sad_tf import *
+
+from transformers import AutoProcessor, AutoModelForCTC
+processor = AutoProcessor.from_pretrained("Akashpb13/Central_kurdish_xlsr")
+model = AutoModelForCTC.from_pretrained("Akashpb13/Central_kurdish_xlsr")
+
+import soundfile as sf
+
+css = """ 
+ textarea { direction: rtl; text-align: right; font-family: Calibri, sans-serif; font-size: 16px;} 
+"""
+
+
+
+seg = Segmenter(ffmpeg_path="ffmpeg",model_path="keras_speech_music_noise_cnn.hdf5" , device="cpu",vad_type="vad")
+
+
+
+def process_segment(args):
+ segment, wav = args
+ start, stop = segment
+ # pp = converter((start, stop))
+ pp = wav[int(start*16000) : int(stop*16000)]
+ input_values =processor(pp, sampling_rate=16000 , return_tensors="pt").input_values
+ with torch.no_grad():
+        logits=model(input_values).logits
+ predicted_ids = torch.argmax(logits, dim=-1)
+ transcription = processor.batch_decode(predicted_ids)[0]   
+ 
+ return start, stop, transcription
+
+def pcm_to_flac(pcm_data, sample_rate=16000):
+ buffer = io.BytesIO()
+ sf.write(buffer, pcm_data, sample_rate, format='FLAC')
+ flac_data = buffer.getvalue() 
+ return flac_data
+
+    
+def transcribe_audio(audio_file):
+
+    text=""
+    isig,wav =  seg(audio_file)
+    isig = filter_output(isig , max_silence=0.5 ,ignore_small_speech_segments=0.1 , max_speech_len=15 ,split_speech_bigger_than=20)   
+    isig = [(a,b) for x,a,b,_,_ in isig]
+    print(isig)
+    results=[]
+    for segment in isig:
+         results.append (process_segment((segment, wav)))
+    for start, stop, tr_beamsearch_lm in results:
+          
+          try:
+              
+              text += ' ' + tr_beamsearch_lm + '\r\n'
+              print(start)
+              print(stop)
+              print(text)
+          except:
+               pass
+    
+    return text
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=transcribe_audio,
+    inputs=gr.Audio(type="filepath"),  
+
+    outputs=gr.Textbox(label="Transcription", elem_id="output-text",interactive=True),
+    title="Soorani Audio Transcription",
+    description="Upload an audio file or record audio to get the transcription.",
+    css=css
+)
+
+# Launch the Gradio app
+interface.launch()