# Transcription - African French Welcome to our model card for African French Transcription. This model card describes our currently deployed transcription model available via our API. ## **Model Details** *Basic information about the model:* Review section 4.1 of the [model cards paper](https://arxiv.org/abs/1810.03993). | Organization | Lelapa AI | | :---- | :---- | | Product | Vulavula | | Model date | 31 October 2023 | | Feature | ASR | | Lang | French (African) | | Domain | Government | | Model Name | Lelapa-X-ASR (African French) | | Model version | 1.0.0 | | Model Type | Fine-Tuned Proprietary Model | **Information about training algorithms, parameters, fairness constraints or other applied approaches, and features:** Proprietary fine-tuning of a base model on African French Transcription data. **License:** Proprietary **Contact**: info@lelapa.ai ## **Intended use** *Use cases that were envisioned during development:* Review section 4.2 of the [model cards paper](https://arxiv.org/abs/1810.03993). ### **Primary intended uses** Intended use is governed by the language and domain of the model. The model is intended to be used transcribe calls conducted in (African) French. The model may not be suitable for the general conversation domain and should be used with extreme caution in high-risk environments. ### ### **Primary intended users** Transcription to enable analysis for downstream tasks for African French: * Enabling search and filter of conversations * Analysis ### **Out-of-scope use cases** All domains and languages outside of the government space for African French. ## **Factors** *Factors could include demographic or phenotypic groups, environmental conditions, technical attributes, or others listed in Section 4.3:* Review section 4.3 of the [model cards paper](https://arxiv.org/abs/1810.03993). ### **Relevant factors** Groups: * Performance across groups is underway. ## **Metrics** *The appropriate metrics to feature in a model card depend on the model being tested. For example, classification systems in which the primary output is a class label differ significantly from systems whose primary output is a score. In all cases, the reported metrics should be determined based on the model’s structure and intended use:* Review section 4.4 of the [model cards paper](https://arxiv.org/abs/1810.03993). ### **Model performance measures** The model is evaluated using WER as well as human evaluation: The models’ performances are measured by both automatic metrics and human evaluation. As an automatic metric, we use the Word Error Rate (WER) which is based on the edit distance also called Levenshtein distance. WER is not a symmetric distance metric, since it measures the number of `operations`: substitution, deletion, insertion, number of correct words needed to leave a reference sentence `A` to a predicted sentence `B`. [Read more](https://huggingface.co/spaces/evaluate-metric/wer). As far as human evaluation is concerned, this stage is performed by paid linguists, native speakers of the languages. Evaluation is also done after post-processing techniques are performed on the outputs. **WER**: Testing on general African French data ### **Decision thresholds** No decision thresholds have been specified ### **Approaches to Uncertainty and Variability** For `fairness`, `robustness`, and `generalization` with respect to languages and datasets, we leveraged standard downsampling and normalization techniques which have proven to be useful. ## **Evaluation data** *All referenced datasets would ideally point to any set of documents that provide visibility into the source and composition of the dataset. Evaluation datasets should include datasets that are publicly available for third-party use. These could be existing datasets or new ones provided alongside the model card analyses to enable further benchmarking.* Review section 4.5 of the [model cards paper](https://arxiv.org/abs/1810.03993). ### **Datasets** * Publicly available datasets in the government domain ### **Motivation** These datasets have been selected because they are open-source, high-quality, and cover the targeted languages \- and utterances are recorded by a variety of speakers living in required regions. These help to capture interesting cultural and linguistic aspects that would be crucial in the development process for better performance. ### **Preprocessing** Audio files are normalised to a sample rate of 16kHz and transcripts are normalised We also make sure to select actual recordings i.e. recordings that are not just noise or blank. ## **Training data** Review section 4.6 of the [model cards paper](https://arxiv.org/abs/1810.03993). * Cameroon: 2003 in Yaoundé, Cameroon. It has recordings from 84 speakers, 48 male and 36 female. * CA16: Gabon in June 2016\. It has recordings from 125 speakers from Cameroon, Chad, Congo and Gabon. * Niger: This part was collected from 23 speakers in Niamey, Niger, Oct. 26-30 2015\. ## **Quantitative analyses** *Quantitative analyses should be disaggregated, that is, broken down by the chosen factors. Quantitative analyses should provide the results of evaluating the model according to the chosen metrics, providing confidence interval values when possible.* Review section 4.7 of the [model cards paper](https://arxiv.org/abs/1810.03993). ### **Unitary results** | WER | | | :---- | :---- | | WER African Accented French | **0.0587** | ### ### **Human evaluation** This is a breakdown of the types of errors we are seeing based on a sample of the evaluation dataset. \*Note: some samples suffered from more than 1 type of error | African\_accented\_french | \# | | :---- | :---- | | Insertion | 35 | | Deletion | 10 | | Substitution | 128 | ### **Intersectional result** In progress ## **Ethical considerations** *This section is intended to demonstrate the ethical considerations that went into model development, surfacing ethical challenges and solutions to stakeholders. The ethical analysis does not always lead to precise solutions, but the process of ethical contemplation is worthwhile to inform on responsible practices and next steps in future work:* Review section 4.8 of the [model cards paper](https://arxiv.org/abs/1810.03993). The model does not contain any personal information.. ## **Caveats and recommendations** *This section should list additional concerns that were not covered in the previous sections.* Review section 4.9 of the [model cards paper](https://arxiv.org/abs/1810.03993). Additional caveats are outlined extensively in our Terms and Conditions.