Adding an example of using pretrained model to predict emotion in local audio file

README.md

@@ -6,4 +6,56 @@ tags:
 - audio
 - HUBert
 ---
-A place to hold the model for easier inference. 
+
+
+Working example of using pretrained model to predict emotion in local audio file
+
+```
+
+def predict_emotion_hubert(audio_file):
+    """ inspired by an example from https://github.com/m3hrdadfi/soxan """
+    from audio_models import HubertForSpeechClassification
+    from transformers import  Wav2Vec2FeatureExtractor, AutoConfig
+    import torch.nn.functional as F
+    import torch
+    import numpy as np
+    from pydub import AudioSegment
+
+    model = HubertForSpeechClassification.from_pretrained("Rajaram1996/Hubert_emotion") # Downloading: 362M
+    feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/hubert-base-ls960")
+    sampling_rate=16000 # defined by the model; must convert mp3 to this rate.
+    config = AutoConfig.from_pretrained("Rajaram1996/Hubert_emotion")
+
+    def speech_file_to_array(path, sampling_rate):
+        # using torchaudio...
+        # speech_array, _sampling_rate = torchaudio.load(path)
+        # resampler = torchaudio.transforms.Resample(_sampling_rate, sampling_rate)
+        # speech = resampler(speech_array).squeeze().numpy()
+        sound = AudioSegment.from_file(path)
+        sound = sound.set_frame_rate(sampling_rate)
+        sound_array = np.array(sound.get_array_of_samples())
+        return sound_array
+
+    sound_array = speech_file_to_array(audio_file, sampling_rate)
+    inputs = feature_extractor(sound_array, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
+    inputs = {key: inputs[key].to("cpu").float() for key in inputs}
+
+    with torch.no_grad():
+        logits = model(**inputs).logits
+
+    scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
+    outputs = [{
+        "emo": config.id2label[i],
+        "score": round(score * 100, 1)}
+        for i, score in enumerate(scores)
+    ]
+    return [row for row in sorted(outputs, key=lambda x:x["score"], reverse=True) if row['score'] != '0.0%'][:2]
+```
+
+```
+
+result = predict_emotion_hubert("male-crying.mp3")
+>>> result
+[{'emo': 'male_sad', 'score': 91.0}, {'emo': 'male_fear', 'score': 4.8}]
+```
+