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runs/Jan12_17-52-52_AxioMBP-PFMH96X-dgonzalez/events.out.tfevents.1736722373.AxioMBP-PFMH96X-dgonzalez.54741.0 +2 -2

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 ---
 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
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### 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]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## 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
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## 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:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
 library_name: transformers
+license: apache-2.0
+base_model: superb/wav2vec2-base-superb-sid
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: transcribe-monkey
+  results: []
 ---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# transcribe-monkey
+This model is a fine-tuned version of [superb/wav2vec2-base-superb-sid](https://huggingface.co/superb/wav2vec2-base-superb-sid) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2787
+- Accuracy: 0.9677
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 5
+### Training results
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.1226        | 1.0   | 212  | 0.4222          | 0.9516   |
+| 0.2144        | 2.0   | 424  | 0.2416          | 0.9516   |
+| 0.0888        | 3.0   | 636  | 0.2240          | 0.9677   |
+| 0.0004        | 4.0   | 848  | 0.3074          | 0.9677   |
+| 0.0204        | 5.0   | 1060 | 0.2787          | 0.9677   |
+### Framework versions
+- Transformers 4.46.3
+- Pytorch 2.3.0
+- Datasets 3.1.0
+- Tokenizers 0.20.3

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