MERaLiON
/

MERaLiON-SpeechEncoder-2

@@ -69,7 +69,7 @@ For details on background, pre-training, tuning experiments and evaluation, plea
 | MERaLiON-SpeechEncoder-v1       | 82.62         | 3.14 | 4.16 | 97.63 | 0.0590 | 91.09 | 5.18 | 5.06 | 68.02 | 98.60 | 88.99 / 23.89        |
 | MERaLiON-SpeechEncoder-2        | 82.72         | 3.40 | 4.96 | 97.57 | 0.0575 | 88.96 | 3.93 | 3.90 | 68.80 | 98.95 | 89.50 / 23.46        |
-SUPERB is an English-based benchmark for speech encoders covering a wide range of downstream speech tasks across domains such as recognition, detection, semantics, speaker, and paralinguistics, where each task is finetuned separately with a frozen encoder.
 MERaLiON-SpeechEncoder-2 is competitive to state-of-the-art, improving slightly against our own v1 model on speaker and paralinguistic tasks.
@@ -77,16 +77,16 @@ MERaLiON-SpeechEncoder-2 is competitive to state-of-the-art, improving slightly
 ### Automatic Speech Recognition (ASR)
 <p align="center">
-  <img src="overall_wer.svg" width="680"/>
-  <img src="audiobench_wer.svg" width="680"/>
-  <img src="fleurs_wer.svg" width="680"/>
 </p>
-Leveraging on the multilingual capabilities of MERaLiON-SpeechEncoder-2, we further finetuned the model for on supervised speech data to produce a lightweight MERaLiON-SpeechEncoder-2-ASR-CTC, which is competitive to models many times its size in transcribing the target languages, while offering much faster inference speeds. It outperforms the popular Whisper large v3 across most languages in [Audiobench](https://huggingface.co/spaces/MERaLiON/AudioBench-Leaderboard) and maintains close perofrmance in FLEURS. Our internal benchmarking, shown in the 'Overall ASR Performance', also contains several private datasets in addition to Audiobench and FLEURS.
-### Direct Use
-The follwing code snippet can be used to directly obtain latent features i.e. encoded speech by forwarding through the model.
 ```python
 import torch
@@ -132,9 +132,9 @@ with torch.no_grad():
                 output_hidden_states=True)
 ```
-### Downstream Use
-Speech encoders are normally used in finetuning setups to provide the frontend to downstream speech applications. We provide an example below of an ASR finetuning setup with Huggingface. Please refer to this [blog](https://huggingface.co/blog/fine-tune-w2v2-bert) for the full ASR finetuning recipe with Huggingface Trainer. Alternatively, the Huggingface model can be loaded to any other frameworks such as Pytorch or ESPnet for custom finetuning loops.
 ```python
 import torch
@@ -207,177 +207,25 @@ model = AutoModelForCTC.from_pretrained(
 model = model.to(device)
 ```
-### Model Sources [optional]
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-## Uses
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-### Out-of-Scope Use
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-## Evaluation
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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 | MERaLiON-SpeechEncoder-v1       | 82.62         | 3.14 | 4.16 | 97.63 | 0.0590 | 91.09 | 5.18 | 5.06 | 68.02 | 98.60 | 88.99 / 23.89        |
 | MERaLiON-SpeechEncoder-2        | 82.72         | 3.40 | 4.96 | 97.57 | 0.0575 | 88.96 | 3.93 | 3.90 | 68.80 | 98.95 | 89.50 / 23.46        |
+[SUPERB](https://superbbenchmark.org/) is an English-based benchmark for speech encoders covering a wide range of downstream speech tasks across domains such as recognition, detection, semantics, speaker, and paralinguistics, where each task is finetuned separately with a frozen encoder.
 MERaLiON-SpeechEncoder-2 is competitive to state-of-the-art, improving slightly against our own v1 model on speaker and paralinguistic tasks.
 ### Automatic Speech Recognition (ASR)
 <p align="center">
+  <img src="overall_wer.svg" width="700"/>
+  <img src="audiobench_wer.svg" width="700"/>
+  <img src="fleurs_wer.svg" width="700"/>
 </p>
+Leveraging on the multilingual capabilities of MERaLiON-SpeechEncoder-2, we further finetuned the model for on supervised speech data to produce a lightweight MERaLiON-SpeechEncoder-2-ASR-CTC, which is competitive to models many times its size in transcribing the target languages, while offering much faster inference speeds. It outperforms the popular Whisper large v3 across most languages in [Audiobench](https://huggingface.co/spaces/MERaLiON/AudioBench-Leaderboard) and maintains close performance in FLEURS. Our internal benchmarking, shown in the 'Overall ASR Performance', also contains several private datasets in addition to Audiobench and FLEURS.
+## Direct Use
+The following code snippet can be used to directly obtain latent features i.e. encoded speech by forwarding through the model. Inputs into the model are expected to be 80-dimensional Mel-spectrogram features transformed from 16kHz sampled audio. The AutoFeatureExtractor method can carry out the conversion.
 ```python
 import torch
                 output_hidden_states=True)
 ```
+## Downstream Use
+Speech encoders are normally used in finetuning setups to provide the frontend to downstream speech applications. We provide an example below of an ASR finetuning setup with Huggingface. Please refer to this [blog](https://huggingface.co/blog/fine-tune-w2v2-bert) for the full ASR finetuning recipe using Huggingface Trainer. Alternatively, the Huggingface model can be loaded to any other frameworks such as Pytorch or ESPnet for custom finetuning loops.
 ```python
 import torch
 model = model.to(device)
 ```
+### Compute and Infrastructure
+MERaLiON-SpeechEncoder-2 was trained on the [**ASPIRE 2A+**](https://help.nscc.sg/aspire2aplus/about/) Supercomputer Cluster, provided by [**National Supercomputing Centre (NSCC)**](https://www.nscc.sg/), Singapore.
+MERaLiON-SpeechEncoder-2 was trained with 64 H100 GPUs across 8 nodes for collectively around 3.5 million steps. Training time took approximately 15 days.
+## Citation
+If you find our work useful, please cite our technical report:
+```
+@misc{huzaifah2024speechfoundationmodelsingapore,
+      title={MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond},
+      author={{MERaLiON Team}},
+      year={2024},
+      eprint={2412.11538},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2412.11538},
+}
+```