Upload app

app.py

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+# -*- coding: utf-8 -*-
+"""2-Copy1.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1UkxKUpI5tPpdFrJIUFWSlk4LlD75Qgf6
+"""
+
+!pip install -q gradio
+!pip install transformers
+
+import gradio as gr
+import librosa
+import torch
+import torchaudio
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import numpy as np
+
+processor = Wav2Vec2Processor.from_pretrained("maher13/arabic-iti")
+model = Wav2Vec2ForCTC.from_pretrained("maher13/arabic-iti").eval()
+
+def asr_transcript(audio_file, audio_file2):
+    transcript = ""
+
+    if audio_file : 
+      wav, sr = librosa.load(audio_file.name, sr=16000)
+ 
+      input_values = processor(wav, sampling_rate=16000, return_tensors="pt", padding=True).input_values
+      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 : 
+      wav, sr = librosa.load(audio_file2.name, sr=16000)
+
+      input_values = processor(wav, sampling_rate=16000, return_tensors="pt", padding=True).input_values
+      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)
+gradio_ui.launch(share=True)
+