Update app.py

app.py

@@ -3,10 +3,14 @@ import gradio as gr
 import torch
 import torchaudio
 from datasets import load_dataset
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, pipeline
 import pandas as pd
 from sklearn.model_selection import train_test_split
 
+from noisereduce.torchgate import TorchGate as TG
+import re
+from pydub import AudioSegment
+
 
 
 # processor = Wav2Vec2Processor.from_pretrained("RikeshSilwal/wav2vec2-nepali")
@@ -22,21 +26,42 @@ import numpy as np
 
 
 
-def transcribe_audio(audio_file):
-  input_arr, sampling_rate =torchaudio.load(audio_file)
-  resampler = Resample(orig_freq=sampling_rate, new_freq=16000)
-  input_arr = resampler(input_arr).squeeze().numpy()
-  sampling_rate = 16000
-  inputs = processor(input_arr, sampling_rate=16_000, return_tensors="pt", padding=True)
+# def transcribe_audio(audio_file):
+#   input_arr, sampling_rate =torchaudio.load(audio_file)
+#   resampler = Resample(orig_freq=sampling_rate, new_freq=16000)
+#   input_arr = resampler(input_arr).squeeze().numpy()
+#   sampling_rate = 16000
+#   inputs = processor(input_arr, sampling_rate=16_000, return_tensors="pt", padding=True)
 
-  with torch.no_grad():
-    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
+#   with torch.no_grad():
+#     logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
 
-  predicted_ids = torch.argmax(logits, dim=-1)
+#   predicted_ids = torch.argmax(logits, dim=-1)
 
-  predicted_words=  processor.batch_decode(predicted_ids)
+#   predicted_words=  processor.batch_decode(predicted_ids)
 
-  return predicted_words[0]
+#   return predicted_words[0]
+
+def transcribe_audio(audio_file):
+    audio = AudioSegment.from_wav(audio_file)
+
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+    input_arr, sampling_rate =torchaudio.load(audio_file)
+    # Create TorchGating instance
+    tg = TG(sr=sampling_rate, nonstationary=True).to(device)
+    try:
+      input_arr = tg(input_arr)
+    except:
+      input_arr = input_arr
+    if sampling_rate != 16000:
+      resampler = torchaudio.transforms.Resample(orig_freq=sampling_rate, new_freq=16000)
+      input_arr = resampler(input_arr).squeeze().numpy()
+
+    recognizer = pipeline("automatic-speech-recognition", model="Harveenchadha/vakyansh-wav2vec2-nepali-nem-130")
+    prediction = recognizer(input_arr, chunk_length_s=5, stride_length_s=(2,1))
+    prediction = recognizer(input_arr)
+    prediction = re.sub('[<s>]' , '' , str(prediction['text']))
 
 audio_input = gr.inputs.Audio(source="upload", type="filepath")