Upload app.py

app.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[ ]:
+
+
+import soundfile as sf
+import torch
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import argparse
+from glob import glob
+import torchaudio
+import subprocess
+import gradio as gr
+
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+
+def get_filename(wav_file):
+    filename_local = wav_file.split('/')[-1][:-4]
+    filename_new = '/tmp/'+filename_local+'_16.wav'
+    
+    
+    subprocess.call(["sox {} -r {} -b 16 -c 1 {}".format(wav_file, str(16000), filename_new)], shell=True)
+    return filename_new
+
+def parse_transcription(wav_file):
+    # load pretrained model
+    processor = Wav2Vec2Processor.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-english")
+    model = Wav2Vec2ForCTC.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-english")
+
+    # load audio
+
+    
+    wav_file = get_filename(wav_file.name)
+    audio_input, sample_rate = sf.read(wav_file)
+    #test_file = resampler(test_file[0])
+
+    # pad input values and return pt tensor
+    input_values = processor(audio_input, sampling_rate=16_000, return_tensors="pt").input_values
+
+    # INFERENCE
+    # retrieve logits & take argmax
+    logits = model(input_values).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+
+    # transcribe
+    transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
+    return transcription
+
+
+# In[ ]:
+
+
+import gradio as gr
+title = "Speech-to-Text-English"
+description = "Upload a English audio clip, and let AI do the hard work of transcribing."
+
+gr.Interface( 
+    parse_transcription,
+    title=title,
+    inputs=gr.inputs.Audio(label="Record Audio File", type="file", source = "microphone"),
+    description=description, outputs = "text").launch(inline = False)
+