Update app.txt

app.txt

@@ -1,10 +1,30 @@
+import torch
 import torchaudio
-import librosa
 from transformers import Wav2Vec2ForCTC,Wav2Vec2Processor,pipeline
+
 processor = Wav2Vec2Processor.from_pretrained(model_name_or_path)
 model = Wav2Vec2ForCTC.from_pretrained("m3hrdadfi/wav2vec2-large-xlsr-persian")
-def ASR(Audio):
-    audiofile=torchaudio.load(Audio,16000)
 
-iface = gr.Interface(fn=ASR, inputs="audio", outputs="text")
+def speech_file_to_array_fn(path, sampling_rate):
+    speech_array, _sampling_rate = torchaudio.load(path)
+    resampler = torchaudio.transforms.Resample(_sampling_rate)
+    speech = resampler(speech_array).squeeze().numpy()
+    return speech
+
+def predict(path, sampling_rate):
+    speech = speech_file_to_array_fn(path, sampling_rate)
+    inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
+    inputs = {key: inputs[key].to(device) for key in inputs}
+
+    with torch.no_grad():
+        logits = model(**inputs).logits
+
+    scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
+    outputs = [{"Label": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
+    return outputs
+
+def SER(Audio):
+    return predict(Audio,16000)
+
+iface = gr.Interface(fn=SER, inputs="audio", outputs="text")
 iface.launch(share=False)