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--- |
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license: mit |
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inference: false |
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tags: |
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- music |
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--- |
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# Introduction to our series work |
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The development log of our Music Audio Pre-training (m-a-p) model family: |
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- 17/03/2023: we release two advanced music understanding models, [MERT-v1-95M](https://huggingface.co/m-a-p/MERT-v1-95M) and [MERT-v1-330M](https://huggingface.co/m-a-p/MERT-v1-330M) , trained with new paradigm and dataset. They outperform the previous models and can better generalize to more tasks. |
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- 14/03/2023: we retrained the MERT-v0 model with open-source-only music dataset [MERT-v0-public](https://huggingface.co/m-a-p/MERT-v0-public) |
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- 29/12/2022: a music understanding model [MERT-v0](https://huggingface.co/m-a-p/MERT-v0) trained with **MLM** paradigm, which performs better at downstream tasks. |
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- 29/10/2022: a pre-trained MIR model [music2vec](https://huggingface.co/m-a-p/music2vec-v1) trained with **BYOL** paradigm. |
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Here is a table for quick model pick-up: |
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| Name | Pre-train Paradigm | Training Data (hour) | Pre-train Context (second) | Model Size | Transformer Layer-Dimension | Feature Rate | Sample Rate | Release Date | |
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| ------------------------------------------------------------ | ------------------ | -------------------- | ---------------------------- | ---------- | --------------------------- | ------------ | ----------- | ------------ | |
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| [MERT-v1-330M](https://huggingface.co/m-a-p/MERT-v1-330M) | MLM | 160K | 5 | 330M | 24-1024 | 75 Hz | 24K Hz | 17/03/2023 | |
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| [MERT-v1-95M](https://huggingface.co/m-a-p/MERT-v1-95M) | MLM | 20K | 5 | 95M | 12-768 | 75 Hz | 24K Hz | 17/03/2023 | |
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| [MERT-v0-public](https://huggingface.co/m-a-p/MERT-v0-public) | MLM | 900 | 5 | 95M | 12-768 | 50 Hz | 16K Hz | 14/03/2023 | |
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| [MERT-v0](https://huggingface.co/m-a-p/MERT-v0) | MLM | 1000 | 5 | 95 M | 12-768 | 50 Hz | 16K Hz | 29/12/2023 | |
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| [music2vec-v1](https://huggingface.co/m-a-p/music2vec-v1) | BYOL | 1000 | 30 | 95 M | 12-768 | 50 Hz | 16K Hz | 30/10/2022 | |
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## Explanation |
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The m-a-p models share the similar model architecture and the most distinguished difference is the paradigm in used pre-training. Other than that, there are several nuance technical configuration needs to know before using: |
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- **Model Size**: the number of parameters that would be loaded to memory. Please select the appropriate size fitting your hardware. |
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- **Transformer Layer-Dimension**: The number of transformer layers and the corresponding feature dimensions can be outputted from our model. This is marked out because features extracted by **different layers could have various performance depending on tasks**. |
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- **Feature Rate**: Given a 1-second audio input, the number of features output by the model. |
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- **Sample Rate**: The frequency of audio that the model is trained with. |
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# Introduction to MERT-v1 |
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Compared to MERT-v0, we introduce multiple new things in the MERT-v1 pre-training: |
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- Change the pseudo labels to 8 codebooks from [encodec](https://github.com/facebookresearch/encodec), which potentially has higher quality and empower our model to support music generation. |
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- MLM prediction with in-batch noise mixture. |
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- Train with higher audio frequency (24K Hz). |
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- Train with more audio data (up to 160 thousands of hours). |
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- More available model sizes 95M and 330M. |
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More details will be written in our coming-soon paper. |
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# Model Usage |
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```python |
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# from transformers import Wav2Vec2Processor |
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from transformers import Wav2Vec2FeatureExtractor |
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from transformers import AutoModel |
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import torch |
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from torch import nn |
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import torchaudio.transforms as T |
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from datasets import load_dataset |
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# loading our model weights |
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model = AutoModel.from_pretrained("m-a-p/MERT-v1-330M", trust_remote_code=True) |
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# loading the corresponding preprocessor config |
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processor = Wav2Vec2FeatureExtractor.from_pretrained("m-a-p/MERT-v1-330M",trust_remote_code=True) |
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# load demo audio and set processor |
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dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") |
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dataset = dataset.sort("id") |
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sampling_rate = dataset.features["audio"].sampling_rate |
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resample_rate = processor.sampling_rate |
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# make sure the sample_rate aligned |
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if resample_rate != sampling_rate: |
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print(f'setting rate from {sampling_rate} to {resample_rate}') |
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resampler = T.Resample(sampling_rate, resample_rate) |
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else: |
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resampler = None |
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# audio file is decoded on the fly |
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if resampler is None: |
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input_audio = dataset[0]["audio"]["array"] |
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else: |
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input_audio = resampler(torch.from_numpy(dataset[0]["audio"]["array"])) |
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inputs = processor(input_audio, sampling_rate=resample_rate, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs, output_hidden_states=True) |
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# take a look at the output shape, there are 25 layers of representation |
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# each layer performs differently in different downstream tasks, you should choose empirically |
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all_layer_hidden_states = torch.stack(outputs.hidden_states).squeeze() |
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print(all_layer_hidden_states.shape) # [25 layer, Time steps, 1024 feature_dim] |
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# for utterance level classification tasks, you can simply reduce the representation in time |
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time_reduced_hidden_states = all_layer_hidden_states.mean(-2) |
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print(time_reduced_hidden_states.shape) # [25, 1024] |
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# you can even use a learnable weighted average representation |
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aggregator = nn.Conv1d(in_channels=25, out_channels=1, kernel_size=1) |
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weighted_avg_hidden_states = aggregator(time_reduced_hidden_states.unsqueeze(0)).squeeze() |
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print(weighted_avg_hidden_states.shape) # [1024] |
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``` |
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# Citation |
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```shell |
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@article{li2022large, |
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title={Large-Scale Pretrained Model for Self-Supervised Music Audio Representation Learning}, |
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author={Li, Yizhi and Yuan, Ruibin and Zhang, Ge and Ma, Yinghao and Lin, Chenghua and Chen, Xingran and Ragni, Anton and Yin, Hanzhi and Hu, Zhijie and He, Haoyu and others}, |
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year={2022} |
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} |
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``` |
