---
library_name: transformers
language:
- en
- zh
base_model:
- superb/wav2vec2-base-superb-sid
---

# Model Card for Model ID

The model is intended to speaker identification for audio segments taken from the Mandarin Monkey podcast.
It was created based the speakerbox code. https://councildataproject.org/speakerbox/



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** jdalegonzalez
- **Funded by [optional]:** None. sigh
- **Model type:** Wave2Vec audio classifier
- **Language(s) (NLP):** English and Chinese
- **License:** Meh?
- **Finetuned from model:** superb/wav2vec2-base-superb-sid

## Uses

Right now, the only thing the model will do is identify which speaker (between the two hosts of Mandarin Monkey)
is likely speaking a specific audio clip.  In the future, it could be expanded to support
more speakers, more podcasts, etc...

### Direct Use

I'm not sure what direct use is possible other than diarizing a Mandarin Monkey podcast.

[More Information Needed]

### Out-of-Scope Use

Really, almost any use other than identifying which host is speaking a particular
audio clip is out-of-scope.

[More Information Needed]

## Bias, Risks, and Limitations

There is no warranty expressed or implied.  It works for me.  It may do 
nothing for you.  This is experimental and shouldn't be the basis
for any commericial activity.

[More Information Needed]

### Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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[More Information Needed]

## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Model Card Authors [optional]

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## Model Card Contact

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