TKU410410103
/

hubert-large-japanese-asr

@@ -1,201 +1,103 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## 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:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-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]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+license: apache-2.0
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- wer
+- cer
+model-index:
+- name: hubert-base-ser
+  results: []
+datasets:
+- reazon-research/reazonspeech
+- mozilla-foundation/common_voice_11_0
+language:
+- ja
 ---
+<!-- 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. -->
+# hubert-large-asr
+This model is a fine-tuned version of [rinna/japanese-hubert-large](https://huggingface.co/rinna/japanese-hubert-large) ASR. Initially fine-tuned on the Reazonspeech(small) dataset, it was subsequently further fine-tuned on the common_voice_11_0 dataset for ASR tasks.
+## Acknowledgments
+This model's fine-tuning approach was inspired by and references the training methodology used in [vumichien/wav2vec2-large-xlsr-japanese-hiragana](https://huggingface.co/vumichien/wav2vec2-large-xlsr-japanese-hiragana).
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+The model was fine-tuned in two main stages, first on the Reazonspeech dataset, followed by the common_voice_11_0 dataset. Details of the training steps and results are as follows:
+### Training on Reazonspeech
+The initial fine-tuning on the Reazonspeech(small) dataset was carried out with the following performance metrics:
+| Step  | Training Loss | Validation Loss | WER    |
+|-------|---------------|-----------------|--------|
+| 1000  | 12.29880      | 3.610288        | 1.00000|
+| 2000  | 3.601800      | 3.505306        | 1.00000|
+| 3000  | 2.80300       | 1.948012        | 0.722361|
+| 4000  | 1.961500      | 1.545842        | 0.558738|
+| 5000  | 1.712000      | 1.420027        | 0.509049|
+| 6000  | 1.565500      | 1.235171        | 0.466279|
+| 7000  | 1.504900      | 1.160565        | 0.461829|
+| 8000  | 1.409800      | 1.088012        | 0.427435|
+| 9000  | 1.358800      | 1.097211        | 0.409861|
+| 10000 | 1.318600      | 1.062294        | 0.403694|
+| 11000 | 1.258500      | 1.026783        | 0.385464|
+| 12000 | 1.245100      | 1.024860        | 0.379845|
+| 13000 | 1.217700      | 0.985201        | 0.375634|
+| 14000 | 1.187900      | 0.977686        | 0.367163|
+| 15000 | 1.168100      | 0.978529        | 0.363656|
+| 16000 | 1.135800      | 0.965668        | 0.363942|
+| 17000 | 1.140600      | 0.953237        | 0.360912|
+### Training on common_voice_11_0
+After fine-tuning on Reazonspeech, further fine-tuning was performed on the common_voice_11_0 dataset, leading to the following results:
+| Step | Training Loss | Validation Loss | WER    |
+|------|---------------|-----------------|--------|
+| 1000 | 1.08950       | 0.49275         | 0.302035|
+| 2000 | 0.86100       | 0.45113         | 0.266950|
+| 3000 | 0.76240       | 0.442281        | 0.244981|
+| 4000 | 0.70170       | 0.411666        | 0.234287|
+| 5000 | 0.66400       | 0.411769        | 0.227942|
+| 6000 | 0.63810       | 0.413067        | 0.225690|
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 1e-4
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 10
+- lr_scheduler_type: linear
+### Test results
+WER: 22.705487%
+CER: 9.399390%
+### Framework versions
+- Transformers 4.39.1
+- Pytorch 2.2.1+cu118
+- Datasets 2.17.1