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* Restore broken merge * Additional args, DDP, remove CommonLanguage * Update examples for V100, add training results * Style * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Remove custom datasets for simplicity, apply suggestions from code review * Add the attention_mask flag, reorganize README Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
127 lines
4.2 KiB
Markdown
127 lines
4.2 KiB
Markdown
<!---
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Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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# Audio classification examples
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The following examples showcase how to fine-tune `Wav2Vec2` for audio classification using PyTorch.
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Speech recognition models that have been pretrained in unsupervised fashion on audio data alone,
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*e.g.* [Wav2Vec2](https://huggingface.co/transformers/master/model_doc/wav2vec2.html),
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[HuBERT](https://huggingface.co/transformers/master/model_doc/hubert.html),
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[XLSR-Wav2Vec2](https://huggingface.co/transformers/master/model_doc/xlsr_wav2vec2.html), have shown to require only
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very little annotated data to yield good performance on speech classification datasets.
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## Single-GPU
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The following command shows how to fine-tune [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the 🗣️ [Keyword Spotting subset](https://huggingface.co/datasets/superb#ks) of the SUPERB dataset.
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```bash
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python run_audio_classification.py \
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--model_name_or_path facebook/wav2vec2-base \
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--dataset_name superb \
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--dataset_config_name ks \
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--output_dir wav2vec2-base-keyword-spotting \
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--overwrite_output_dir \
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--remove_unused_columns False \
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--do_train \
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--do_eval \
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--fp16 \
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--learning_rate 3e-5 \
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--max_length_seconds 1 \
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--warmup_ratio 0.1 \
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--num_train_epochs 5 \
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--per_device_train_batch_size 32 \
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--gradient_accumulation_steps 4 \
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--per_device_eval_batch_size 32 \
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--dataloader_num_workers 4 \
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--logging_strategy steps \
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--logging_steps 10 \
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--evaluation_strategy epoch \
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--save_strategy epoch \
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--load_best_model_at_end True \
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--save_total_limit 3 \
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--seed 0 \
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--push_to_hub
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```
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On a single V100 GPU (16GB), this script should run in ~10 minutes and yield accuracy of **98.4%**.
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👀 See the results here: [anton-l/wav2vec2-base-keyword-spotting](https://huggingface.co/anton-l/wav2vec2-base-keyword-spotting)
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## Multi-GPU
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The following command shows how to fine-tune [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) for 🌎 **Language Identification** on the [CommonLanguage dataset](https://huggingface.co/datasets/anton-l/common_language).
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```bash
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python run_audio_classification.py \
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--model_name_or_path facebook/wav2vec2-base \
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--dataset_name anton-l/common_language \
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--audio_column_name path \
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--label_column_name language \
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--output_dir wav2vec2-base-lang-id \
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--overwrite_output_dir \
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--remove_unused_columns False \
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--do_train \
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--do_eval \
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--fp16 \
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--learning_rate 3e-5 \
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--max_length_seconds 16 \
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--attention_mask False \
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--warmup_ratio 0.1 \
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--num_train_epochs 10 \
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--per_device_train_batch_size 8 \
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--gradient_accumulation_steps 4 \
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--per_device_eval_batch_size 1 \
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--dataloader_num_workers 8 \
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--logging_strategy steps \
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--logging_steps 10 \
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--evaluation_strategy epoch \
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--save_strategy epoch \
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--load_best_model_at_end True \
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--save_total_limit 3 \
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--seed 0 \
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--push_to_hub
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```
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On 4 V100 GPUs (16GB), this script should run in ~1 hour and yield accuracy of **79.45%**.
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👀 See the results here: [anton-l/wav2vec2-base-lang-id](https://huggingface.co/anton-l/wav2vec2-base-lang-id)
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## Sharing your model on 🤗 Hub
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0. If you haven't already, [sign up](https://huggingface.co/join) for a 🤗 account
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1. Make sure you have `git-lfs` installed and git set up.
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```bash
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$ apt install git-lfs
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```
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2. Log in with your HuggingFace account credentials using `huggingface-cli`
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```bash
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$ huggingface-cli login
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# ...follow the prompts
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```
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3. When running the script, pass the following arguments:
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```bash
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python run_audio_classification.py \
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--push_to_hub \
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--hub_model_id <username/model_id> \
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...
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``` |