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            ---
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            - **Model type:** [More Information Needed]
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            - **Language(s) (NLP):** [More Information Needed]
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            - **License:** [More Information Needed]
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            - **Finetuned from model [optional]:** [More Information Needed]
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            ### Model Sources [optional]
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            <!-- Provide the basic links for the model. -->
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            - **Repository:** [More Information Needed]
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            - **Paper [optional]:** [More Information Needed]
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            - **Demo [optional]:** [More Information Needed]
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            ## Uses
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            <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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            ### Direct Use
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            <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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            [More Information Needed]
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            ### Downstream Use [optional]
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            <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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            [More Information Needed]
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            ### Out-of-Scope Use
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            <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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            [More Information Needed]
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            ## Bias, Risks, and Limitations
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            <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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            [More Information Needed]
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            ### Recommendations
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            <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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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            <!-- This should link to a Data 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. -->
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            [More Information Needed]
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            ### Training Procedure 
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            <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the 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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            #### Speeds, Sizes, Times [optional]
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            <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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            [More Information Needed]
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            ## Evaluation
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            <!-- This section describes the evaluation protocols and provides the results. -->
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            ### Testing Data, Factors & Metrics
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            #### Testing Data
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            <!-- This should link to a Data Card if possible. -->
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            [More Information Needed]
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            #### Factors
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            <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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            [More Information Needed]
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            #### Metrics
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            <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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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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            <!-- Relevant interpretability work for the model goes here -->
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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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            - **Hardware Type:** [More Information Needed]
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            - **Hours used:** [More Information Needed]
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            - **Cloud Provider:** [More Information Needed]
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            - **Compute Region:** [More Information Needed]
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            - **Carbon Emitted:** [More Information Needed]
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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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            ### Compute Infrastructure
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            [More Information Needed]
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            #### Hardware
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            #### Software
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            ## Citation [optional]
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            <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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            **BibTeX:**
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            [More Information Needed]
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            **APA:**
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            [More Information Needed]
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            ## Glossary [optional]
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            [More Information Needed]
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            ## More Information [optional]
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            [More Information Needed]
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            ## Model Card Authors [optional]
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            [More Information Needed]
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            ## Model Card Contact
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                        value: 17.634350000973198
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            ---
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            # Respeecher/ukrainian-data2vec-asr
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            This model is a fine-tuned version of [Respeecher/ukrainian-data2vec](https://huggingface.co/Respeecher/ukrainian-data2vec) on the [Common Voice 11.0 dataset Ukrainian Train part](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/uk/train).
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            It achieves the following results:
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            - eval_wer: 17.634350000973198
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            - test_wer: 17.042283338786351
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            ## How to Get Started with the Model
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            ```python
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            from transformers import AutoProcessor, Data2VecAudioForCTC
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            import torch
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            from datasets import load_dataset, Audio
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            dataset = load_dataset("mozilla-foundation/common_voice_11_0", "uk", split="test")
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            # Resample
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            dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
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            processor = AutoProcessor.from_pretrained("Respeecher/ukrainian-data2vec-asr")
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            model = Data2VecAudioForCTC.from_pretrained("Respeecher/ukrainian-data2vec-asr")
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            model.eval()
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            sampling_rate = dataset.features["audio"].sampling_rate
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            inputs = processor(dataset[1]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
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            with torch.no_grad():
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                logits = model(**inputs).logits
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            predicted_ids = torch.argmax(logits, dim=-1)
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            transcription = processor.batch_decode(predicted_ids)
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            transcription[0]
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            ```
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            ## Training Details
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            Training code and instructions are available on [our github](https://github.com/respeecher/ukrainian_asr)
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