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