funasr1.0 emotion2vec

游雁

2024-01-08 e8590bb1e9a26ce9bf8f0991d53b5ea4f0d77627

funasr1.0 emotion2vec

 examples/industrial_data_pretraining/emotion2vec/demo.py

@@ -5,7 +5,7 @@

from funasr import AutoModel

model = AutoModel(model="/Users/zhifu/Downloads/modelscope_models/emotion2vec_base")
model = AutoModel(model="../modelscope_models/emotion2vec_base")

res = model(input="/Users/zhifu/Downloads/modelscope_models/emotion2vec_base/example/test.wav")
res = model(input="../modelscope_models/emotion2vec_base/example/test.wav")
print(res)

 funasr/models/emotion2vec/model.py

@@ -1,5 +1,11 @@
#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)
# Modified from https://github.com/ddlBoJack/emotion2vec/tree/main

import logging
import os
from functools import partial
import numpy as np

@@ -21,7 +27,11 @@

@tables.register("model_classes", "Emotion2vec")
class Emotion2vec(nn.Module):

    """
    Author: Ziyang Ma, Zhisheng Zheng, Jiaxin Ye, Jinchao Li, Zhifu Gao, Shiliang Zhang, Xie Chen
    emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation
    https://arxiv.org/abs/2312.15185
    """
    def __init__(self, **kwargs):
        super().__init__()
        # import pdb; pdb.set_trace()
@@ -196,6 +206,9 @@
        time2 = time.perf_counter()
        meta_data["load_data"] = f"{time2 - time1:0.3f}"
        results = []
        output_dir = kwargs.get("output_dir")
        if output_dir:
            os.makedirs(output_dir, exist_ok=True)
        for i, wav in enumerate(audio_sample_list):
            source = wav.to(device=kwargs["device"])
            if self.cfg.normalize:
@@ -211,5 +224,7 @@
            
            result_i = {"key": key[i], "feats": feats}
            results.append(result_i)
            if output_dir:
                np.save(os.path.join(output_dir, "{}.npy".format(key[i])), feats)
            
        return results, meta_data