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Add Wav2Vec2 & Hubert ForSequenceClassification (#13153)
* Add hubert classifier + tests * Add hubert classifier + tests * Dummies for all classification tests * Wav2Vec2 classifier + ER test * Fix hubert integration tests * Add hubert IC * Pass tests for all classification tasks on Hubert * Pass all tests + copies * Move models to the SUPERB org
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src/transformers/models/hubert/convert_hubert_original_s3prl_checkpoint_to_pytorch.py
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# coding=utf-8 | ||
# Copyright 2021 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 Hubert checkpoint.""" | ||
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import argparse | ||
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import torch | ||
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from transformers import HubertConfig, HubertForSequenceClassification, Wav2Vec2FeatureExtractor, logging | ||
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logging.set_verbosity_info() | ||
logger = logging.get_logger(__name__) | ||
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SUPPORTED_MODELS = ["UtteranceLevel"] | ||
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@torch.no_grad() | ||
def convert_s3prl_checkpoint(base_model_name, config_path, checkpoint_path, model_dump_path): | ||
""" | ||
Copy/paste/tweak model's weights to transformers design. | ||
""" | ||
checkpoint = torch.load(checkpoint_path, map_location="cpu") | ||
if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS: | ||
raise NotImplementedError(f"The supported s3prl models are {SUPPORTED_MODELS}") | ||
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downstream_dict = checkpoint["Downstream"] | ||
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hf_congfig = HubertConfig.from_pretrained(config_path) | ||
hf_model = HubertForSequenceClassification.from_pretrained(base_model_name, config=hf_congfig) | ||
hf_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( | ||
base_model_name, return_attention_mask=True, do_normalize=False | ||
) | ||
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if hf_congfig.use_weighted_layer_sum: | ||
hf_model.layer_weights.data = checkpoint["Featurizer"]["weights"] | ||
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hf_model.projector.weight.data = downstream_dict["projector.weight"] | ||
hf_model.projector.bias.data = downstream_dict["projector.bias"] | ||
hf_model.classifier.weight.data = downstream_dict["model.post_net.linear.weight"] | ||
hf_model.classifier.bias.data = downstream_dict["model.post_net.linear.bias"] | ||
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hf_feature_extractor.save_pretrained(model_dump_path) | ||
hf_model.save_pretrained(model_dump_path) | ||
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if __name__ == "__main__": | ||
parser = argparse.ArgumentParser() | ||
parser.add_argument( | ||
"--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model." | ||
) | ||
parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.") | ||
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.") | ||
parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.") | ||
args = parser.parse_args() | ||
convert_s3prl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path) |
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