Create app.py

app.py

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+import torch
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
+import torchaudio
+
+def speech_recognition(audio_file_path):
+    tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
+    model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
+
+    waveform, sample_rate = torchaudio.load(audio_file_path)
+
+    if sample_rate != 16000:
+        resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)
+        waveform = resampler(waveform)
+
+    input_values = tokenizer(waveform.squeeze().numpy(), return_tensors="pt", padding="longest").input_values
+    with torch.no_grad():
+        logits = model(input_values).logits
+
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = tokenizer.batch_decode(predicted_ids)
+
+    return transcription[0]