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from fastapi import FastAPI, File, UploadFile |
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from modelscope.pipelines import pipeline |
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from modelscope.utils.constant import Tasks |
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import numpy as np |
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app = FastAPI() |
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mapper = ["angry", "disgust", "fear", "happy", |
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"neutral", "other", "sad", "surprised", "unknown"] |
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inference_pipeline = pipeline( |
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task=Tasks.emotion_recognition, |
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model="iic/emotion2vec_base_finetuned", model_revision="v2.0.4") |
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@app.post("/emotion_recognition") |
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async def emotion_recognition(audio_file: UploadFile = File(...)): |
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audio_bytes = await audio_file.read() |
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rec_result = inference_pipeline( |
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audio_bytes, output_dir="./outputs", granularity="utterance", extract_embedding=False) |
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max_emotion_score = np.argmax(rec_result[0]["scores"]) |
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return { |
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"emotion": mapper[max_emotion_score], |
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"confidence":rec_result[0]["scores"][max_emotion_score] |
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} |
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