app.py

from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import soundfile as sf
import torch
import gradio as gr


# load model and processor
processor = Wav2Vec2Processor.from_pretrained("maher13/arabic-iti")
model = Wav2Vec2ForCTC.from_pretrained("maher13/arabic-iti").eval()
# define function to read in sound file
def map_to_array(file):
   speech, _ = sf.read(file)
   return speech
 
# tokenize
def inference(audio_file, audio_file2):
   if audio_file:
     input_values = processor(map_to_array(audio_file.name), return_tensors="pt", padding="longest").input_values  # Batch size 1
     logits = model(input_values).logits
  
      with torch.no_grad():
        predicted_ids = torch.argmax(logits, dim=-1)
      predicted_ids[predicted_ids == -100] = processor.tokenizer.pad_token_id
      transcription1 = processor.tokenizer.batch_decode(predicted_ids)[0]
    else:
      transcription1 = "N/A"
     
    if audio_file2:
      input_values = processor(map_to_array(audio_file2.name), return_tensors="pt", padding="longest").input_values  # Batch size 1
      logits = model(input_values).logits
  
      with torch.no_grad():
        predicted_ids = torch.argmax(logits, dim=-1)
      predicted_ids[predicted_ids == -100] = processor.tokenizer.pad_token_id
      transcription2 = processor.tokenizer.batch_decode(predicted_ids)[0]
    else :
      transcription2 = "N/A"
     
     
     return transcription1, transcription2
     
     
gradio_ui = gr.Interface(
    fn=asr_transcript,
    title="Speech to Text Graduation project \n sponsored by TensorGraph",
    inputs=
    [
    gr.inputs.Audio(source = 'microphone', type="file", optional = True),
    gr.inputs.Audio(source = 'upload', type="file", optional = True) 
    ],
    outputs=[
             gr.outputs.Textbox(label="Auto-Transcript"),
             gr.outputs.Textbox(label="Auto-Transcript")
             ],
)
gradio_ui.launch(share=True)