theimageconvert2
/
transformers_4_35_0
/models
/audio_spectrogram_transformer
/convert_audio_spectrogram_transformer_original_to_pytorch.py
# coding=utf-8 | |
# Copyright 2022 The HuggingFace Inc. team. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
"""Convert Audio Spectrogram Transformer checkpoints from the original repository. URL: https://github.com/YuanGongND/ast""" | |
import argparse | |
import json | |
from pathlib import Path | |
import torch | |
import torchaudio | |
from datasets import load_dataset | |
from huggingface_hub import hf_hub_download | |
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification | |
from transformers.utils import logging | |
logging.set_verbosity_info() | |
logger = logging.get_logger(__name__) | |
def get_audio_spectrogram_transformer_config(model_name): | |
config = ASTConfig() | |
if "10-10" in model_name: | |
pass | |
elif "speech-commands" in model_name: | |
config.max_length = 128 | |
elif "12-12" in model_name: | |
config.time_stride = 12 | |
config.frequency_stride = 12 | |
elif "14-14" in model_name: | |
config.time_stride = 14 | |
config.frequency_stride = 14 | |
elif "16-16" in model_name: | |
config.time_stride = 16 | |
config.frequency_stride = 16 | |
else: | |
raise ValueError("Model not supported") | |
repo_id = "huggingface/label-files" | |
if "speech-commands" in model_name: | |
config.num_labels = 35 | |
filename = "speech-commands-v2-id2label.json" | |
else: | |
config.num_labels = 527 | |
filename = "audioset-id2label.json" | |
id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) | |
id2label = {int(k): v for k, v in id2label.items()} | |
config.id2label = id2label | |
config.label2id = {v: k for k, v in id2label.items()} | |
return config | |
def rename_key(name): | |
if "module.v" in name: | |
name = name.replace("module.v", "audio_spectrogram_transformer") | |
if "cls_token" in name: | |
name = name.replace("cls_token", "embeddings.cls_token") | |
if "dist_token" in name: | |
name = name.replace("dist_token", "embeddings.distillation_token") | |
if "pos_embed" in name: | |
name = name.replace("pos_embed", "embeddings.position_embeddings") | |
if "patch_embed.proj" in name: | |
name = name.replace("patch_embed.proj", "embeddings.patch_embeddings.projection") | |
# transformer blocks | |
if "blocks" in name: | |
name = name.replace("blocks", "encoder.layer") | |
if "attn.proj" in name: | |
name = name.replace("attn.proj", "attention.output.dense") | |
if "attn" in name: | |
name = name.replace("attn", "attention.self") | |
if "norm1" in name: | |
name = name.replace("norm1", "layernorm_before") | |
if "norm2" in name: | |
name = name.replace("norm2", "layernorm_after") | |
if "mlp.fc1" in name: | |
name = name.replace("mlp.fc1", "intermediate.dense") | |
if "mlp.fc2" in name: | |
name = name.replace("mlp.fc2", "output.dense") | |
# final layernorm | |
if "audio_spectrogram_transformer.norm" in name: | |
name = name.replace("audio_spectrogram_transformer.norm", "audio_spectrogram_transformer.layernorm") | |
# classifier head | |
if "module.mlp_head.0" in name: | |
name = name.replace("module.mlp_head.0", "classifier.layernorm") | |
if "module.mlp_head.1" in name: | |
name = name.replace("module.mlp_head.1", "classifier.dense") | |
return name | |
def convert_state_dict(orig_state_dict, config): | |
for key in orig_state_dict.copy().keys(): | |
val = orig_state_dict.pop(key) | |
if "qkv" in key: | |
key_split = key.split(".") | |
layer_num = int(key_split[3]) | |
dim = config.hidden_size | |
if "weight" in key: | |
orig_state_dict[ | |
f"audio_spectrogram_transformer.encoder.layer.{layer_num}.attention.attention.query.weight" | |
] = val[:dim, :] | |
orig_state_dict[ | |
f"audio_spectrogram_transformer.encoder.layer.{layer_num}.attention.attention.key.weight" | |
] = val[dim : dim * 2, :] | |
orig_state_dict[ | |
f"audio_spectrogram_transformer.encoder.layer.{layer_num}.attention.attention.value.weight" | |
] = val[-dim:, :] | |
else: | |
orig_state_dict[ | |
f"audio_spectrogram_transformer.encoder.layer.{layer_num}.attention.attention.query.bias" | |
] = val[:dim] | |
orig_state_dict[ | |
f"audio_spectrogram_transformer.encoder.layer.{layer_num}.attention.attention.key.bias" | |
] = val[dim : dim * 2] | |
orig_state_dict[ | |
f"audio_spectrogram_transformer.encoder.layer.{layer_num}.attention.attention.value.bias" | |
] = val[-dim:] | |
else: | |
orig_state_dict[rename_key(key)] = val | |
return orig_state_dict | |
def remove_keys(state_dict): | |
ignore_keys = [ | |
"module.v.head.weight", | |
"module.v.head.bias", | |
"module.v.head_dist.weight", | |
"module.v.head_dist.bias", | |
] | |
for k in ignore_keys: | |
state_dict.pop(k, None) | |
def convert_audio_spectrogram_transformer_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub=False): | |
""" | |
Copy/paste/tweak model's weights to our Audio Spectrogram Transformer structure. | |
""" | |
config = get_audio_spectrogram_transformer_config(model_name) | |
model_name_to_url = { | |
"ast-finetuned-audioset-10-10-0.4593": ( | |
"https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1" | |
), | |
"ast-finetuned-audioset-10-10-0.450": ( | |
"https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1" | |
), | |
"ast-finetuned-audioset-10-10-0.448": ( | |
"https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1" | |
), | |
"ast-finetuned-audioset-10-10-0.448-v2": ( | |
"https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1" | |
), | |
"ast-finetuned-audioset-12-12-0.447": ( | |
"https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1" | |
), | |
"ast-finetuned-audioset-14-14-0.443": ( | |
"https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1" | |
), | |
"ast-finetuned-audioset-16-16-0.442": ( | |
"https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1" | |
), | |
"ast-finetuned-speech-commands-v2": ( | |
"https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1" | |
), | |
} | |
# load original state_dict | |
checkpoint_url = model_name_to_url[model_name] | |
state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu") | |
# remove some keys | |
remove_keys(state_dict) | |
# rename some keys | |
new_state_dict = convert_state_dict(state_dict, config) | |
# load 🤗 model | |
model = ASTForAudioClassification(config) | |
model.eval() | |
model.load_state_dict(new_state_dict) | |
# verify outputs on dummy input | |
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62 | |
mean = -4.2677393 if "speech-commands" not in model_name else -6.845978 | |
std = 4.5689974 if "speech-commands" not in model_name else 5.5654526 | |
max_length = 1024 if "speech-commands" not in model_name else 128 | |
feature_extractor = ASTFeatureExtractor(mean=mean, std=std, max_length=max_length) | |
if "speech-commands" in model_name: | |
dataset = load_dataset("speech_commands", "v0.02", split="validation") | |
waveform = dataset[0]["audio"]["array"] | |
else: | |
filepath = hf_hub_download( | |
repo_id="nielsr/audio-spectogram-transformer-checkpoint", | |
filename="sample_audio.flac", | |
repo_type="dataset", | |
) | |
waveform, _ = torchaudio.load(filepath) | |
waveform = waveform.squeeze().numpy() | |
inputs = feature_extractor(waveform, sampling_rate=16000, return_tensors="pt") | |
# forward pass | |
outputs = model(**inputs) | |
logits = outputs.logits | |
if model_name == "ast-finetuned-audioset-10-10-0.4593": | |
expected_slice = torch.tensor([-0.8760, -7.0042, -8.6602]) | |
elif model_name == "ast-finetuned-audioset-10-10-0.450": | |
expected_slice = torch.tensor([-1.1986, -7.0903, -8.2718]) | |
elif model_name == "ast-finetuned-audioset-10-10-0.448": | |
expected_slice = torch.tensor([-2.6128, -8.0080, -9.4344]) | |
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2": | |
expected_slice = torch.tensor([-1.5080, -7.4534, -8.8917]) | |
elif model_name == "ast-finetuned-audioset-12-12-0.447": | |
expected_slice = torch.tensor([-0.5050, -6.5833, -8.0843]) | |
elif model_name == "ast-finetuned-audioset-14-14-0.443": | |
expected_slice = torch.tensor([-0.3826, -7.0336, -8.2413]) | |
elif model_name == "ast-finetuned-audioset-16-16-0.442": | |
expected_slice = torch.tensor([-1.2113, -6.9101, -8.3470]) | |
elif model_name == "ast-finetuned-speech-commands-v2": | |
expected_slice = torch.tensor([6.1589, -8.0566, -8.7984]) | |
else: | |
raise ValueError("Unknown model name") | |
if not torch.allclose(logits[0, :3], expected_slice, atol=1e-4): | |
raise ValueError("Logits don't match") | |
print("Looks ok!") | |
if pytorch_dump_folder_path is not None: | |
Path(pytorch_dump_folder_path).mkdir(exist_ok=True) | |
print(f"Saving model {model_name} to {pytorch_dump_folder_path}") | |
model.save_pretrained(pytorch_dump_folder_path) | |
print(f"Saving feature extractor to {pytorch_dump_folder_path}") | |
feature_extractor.save_pretrained(pytorch_dump_folder_path) | |
if push_to_hub: | |
print("Pushing model and feature extractor to the hub...") | |
model.push_to_hub(f"MIT/{model_name}") | |
feature_extractor.push_to_hub(f"MIT/{model_name}") | |
if __name__ == "__main__": | |
parser = argparse.ArgumentParser() | |
# Required parameters | |
parser.add_argument( | |
"--model_name", | |
default="ast-finetuned-audioset-10-10-0.4593", | |
type=str, | |
help="Name of the Audio Spectrogram Transformer model you'd like to convert.", | |
) | |
parser.add_argument( | |
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." | |
) | |
parser.add_argument( | |
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." | |
) | |
args = parser.parse_args() | |
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) | |