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--- |
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license: cc-by-4.0 |
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language: |
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- en |
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pipeline_tag: automatic-speech-recognition |
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library_name: nemo |
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datasets: |
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- librispeech_asr |
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- fisher_corpus |
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- mozilla-foundation/common_voice_8_0 |
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- National-Singapore-Corpus-Part-1 |
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- vctk |
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- voxpopuli |
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- europarl |
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- multilingual_librispeech |
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thumbnail: null |
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tags: |
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- automatic-speech-recognition |
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- speech |
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- audio |
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- Transducer |
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- TDT |
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- FastConformer |
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- Conformer |
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- pytorch |
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- NeMo |
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- hf-asr-leaderboard |
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widget: |
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- example_title: Librispeech sample 1 |
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src: https://cdn-media.huggingface.co/speech_samples/sample1.flac |
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- example_title: Librispeech sample 2 |
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src: https://cdn-media.huggingface.co/speech_samples/sample2.flac |
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model-index: |
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- name: parakeet-tdt-0.6b-v2 |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: AMI (Meetings test) |
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type: edinburghcstr/ami |
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config: ihm |
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split: test |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 11.16 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Earnings-22 |
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type: revdotcom/earnings22 |
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split: test |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 11.15 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: GigaSpeech |
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type: speechcolab/gigaspeech |
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split: test |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 9.74 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: LibriSpeech (clean) |
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type: librispeech_asr |
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config: other |
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split: test |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 1.69 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: LibriSpeech (other) |
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type: librispeech_asr |
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config: other |
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split: test |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 3.19 |
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- task: |
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type: Automatic Speech Recognition |
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name: automatic-speech-recognition |
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dataset: |
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name: SPGI Speech |
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type: kensho/spgispeech |
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config: test |
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split: test |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 2.17 |
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- task: |
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type: Automatic Speech Recognition |
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name: automatic-speech-recognition |
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dataset: |
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name: tedlium-v3 |
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type: LIUM/tedlium |
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config: release1 |
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split: test |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 3.38 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Vox Populi |
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type: facebook/voxpopuli |
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config: en |
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split: test |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 5.95 |
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metrics: |
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- wer |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). |
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## Model Details |
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### Model Description |
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### Downstream Use [optional] |
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## Bias, Risks, and Limitations |
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[More Information Needed] |
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### Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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[More Information Needed] |
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### Training Procedure |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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## Evaluation |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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[More Information Needed] |
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#### Factors |
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[More Information Needed] |
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#### Metrics |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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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### Model Architecture and Objective |
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[More Information Needed] |
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#### Hardware |
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#### Software |
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**BibTeX:** |
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**APA:** |
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