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#!/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 os
import time
import torch
import logging
import numpy as np
from functools import partial
from omegaconf import OmegaConf
import torch.nn.functional as F
from contextlib import contextmanager
from distutils.version import LooseVersion
from funasr_detach.register import tables
from funasr_detach.models.emotion2vec.modules import AltBlock
from funasr_detach.models.emotion2vec.audio import AudioEncoder
from funasr_detach.utils.load_utils import load_audio_text_image_video
logger = logging.getLogger(__name__)
if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
from torch.cuda.amp import autocast
else:
# Nothing to do if torch<1.6.0
@contextmanager
def autocast(enabled=True):
yield
@tables.register("model_classes", "Emotion2vec")
class Emotion2vec(torch.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()
cfg = OmegaConf.create(kwargs["model_conf"])
self.cfg = cfg
make_layer_norm = partial(
torch.nn.LayerNorm,
eps=cfg.get("norm_eps"),
elementwise_affine=cfg.get("norm_affine"),
)
def make_block(drop_path, dim=None, heads=None):
return AltBlock(
cfg.get("embed_dim") if dim is None else dim,
cfg.get("num_heads") if heads is None else heads,
cfg.get("mlp_ratio"),
qkv_bias=True,
drop=cfg.get("encoder_dropout"),
attn_drop=cfg.get("attention_dropout"),
mlp_drop=cfg.get("activation_dropout"),
post_mlp_drop=cfg.get("post_mlp_drop"),
drop_path=drop_path,
norm_layer=make_layer_norm,
layer_norm_first=cfg.get("layer_norm_first"),
ffn_targets=not cfg.get("end_of_block_targets"),
)
self.alibi_biases = {}
self.modality_encoders = torch.nn.ModuleDict()
enc = AudioEncoder(
cfg.modalities.audio,
cfg.get("embed_dim"),
make_block,
make_layer_norm,
cfg.get("layer_norm_first"),
self.alibi_biases,
)
self.modality_encoders["AUDIO"] = enc
self.ema = None
self.average_top_k_layers = cfg.get("average_top_k_layers")
self.loss_beta = cfg.get("loss_beta")
self.loss_scale = cfg.get("loss_scale")
self.dropout_input = torch.nn.Dropout(cfg.get("dropout_input"))
dpr = np.linspace(
cfg.get("start_drop_path_rate"),
cfg.get("end_drop_path_rate"),
cfg.get("depth"),
)
self.blocks = torch.nn.ModuleList(
[make_block(dpr[i]) for i in range(cfg.get("depth"))]
)
self.norm = None
if cfg.get("layer_norm_first"):
self.norm = make_layer_norm(cfg.get("embed_dim"))
vocab_size = kwargs.get("vocab_size", -1)
self.proj = None
if vocab_size > 0:
self.proj = torch.nn.Linear(cfg.get("embed_dim"), vocab_size)
def forward(
self,
source,
target=None,
id=None,
mode=None,
padding_mask=None,
mask=True,
features_only=False,
force_remove_masked=False,
remove_extra_tokens=True,
precomputed_mask=None,
**kwargs,
):
feature_extractor = self.modality_encoders["AUDIO"]
mask_seeds = None
extractor_out = feature_extractor(
source,
padding_mask,
mask,
remove_masked=not features_only or force_remove_masked,
clone_batch=self.cfg.get("clone_batch") if not features_only else 1,
mask_seeds=mask_seeds,
precomputed_mask=precomputed_mask,
)
x = extractor_out["x"]
encoder_mask = extractor_out["encoder_mask"]
masked_padding_mask = extractor_out["padding_mask"]
masked_alibi_bias = extractor_out.get("alibi_bias", None)
alibi_scale = extractor_out.get("alibi_scale", None)
if self.dropout_input is not None:
x = self.dropout_input(x)
layer_results = []
for i, blk in enumerate(self.blocks):
if (
not self.training
or self.cfg.get("layerdrop", 0) == 0
or (np.random.random() > self.cfg.get("layerdrop", 0))
):
ab = masked_alibi_bias
if ab is not None and alibi_scale is not None:
scale = (
alibi_scale[i]
if alibi_scale.size(0) > 1
else alibi_scale.squeeze(0)
)
ab = ab * scale.type_as(ab)
x, lr = blk(
x,
padding_mask=masked_padding_mask,
alibi_bias=ab,
)
if features_only:
layer_results.append(lr)
if self.norm is not None:
x = self.norm(x)
if features_only:
if remove_extra_tokens:
x = x[:, feature_extractor.modality_cfg.num_extra_tokens :]
if masked_padding_mask is not None:
masked_padding_mask = masked_padding_mask[
:, feature_extractor.modality_cfg.num_extra_tokens :
]
return {
"x": x,
"padding_mask": masked_padding_mask,
"layer_results": layer_results,
"mask": encoder_mask,
}
def extract_features(
self, source, mode=None, padding_mask=None, mask=False, remove_extra_tokens=True
):
res = self.forward(
source,
mode=mode,
padding_mask=padding_mask,
mask=mask,
features_only=True,
remove_extra_tokens=remove_extra_tokens,
)
return res
def inference(
self,
data_in,
data_lengths=None,
key: list = None,
tokenizer=None,
frontend=None,
**kwargs,
):
# if source_file.endswith('.wav'):
# wav, sr = sf.read(source_file)
# channel = sf.info(source_file).channels
# assert sr == 16e3, "Sample rate should be 16kHz, but got {}in file {}".format(sr, source_file)
# assert channel == 1, "Channel should be 1, but got {} in file {}".format(channel, source_file)
granularity = kwargs.get("granularity", "utterance")
extract_embedding = kwargs.get("extract_embedding", True)
if self.proj is None:
extract_embedding = True
meta_data = {}
# extract fbank feats
time1 = time.perf_counter()
audio_sample_list = load_audio_text_image_video(
data_in,
fs=16000,
audio_fs=kwargs.get("fs", 16000),
data_type=kwargs.get("data_type", "sound"),
tokenizer=tokenizer,
)
time2 = time.perf_counter()
meta_data["load_data"] = f"{time2 - time1:0.3f}"
meta_data["batch_data_time"] = len(audio_sample_list[0]) / kwargs.get(
"fs", 16000
)
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:
source = F.layer_norm(source, source.shape)
source = source.view(1, -1)
feats = self.extract_features(source, padding_mask=None)
x = feats["x"]
feats = feats["x"].squeeze(0).cpu().numpy()
if granularity == "frame":
feats = feats
elif granularity == "utterance":
feats = np.mean(feats, axis=0)
if output_dir and extract_embedding:
np.save(os.path.join(output_dir, "{}.npy".format(key[i])), feats)
labels = tokenizer.token_list if tokenizer is not None else []
scores = []
if self.proj:
x = x.mean(dim=1)
x = self.proj(x)
x = torch.softmax(x, dim=-1)
scores = x[0].tolist()
result_i = {"key": key[i], "labels": labels, "scores": scores}
if extract_embedding:
result_i["feats"] = feats
results.append(result_i)
return results, meta_data
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