For example, trainer.max_epochs.Īfter the model is trained, evaluated, and there is a need for fine-tuning, the following command can be used to fine-tune the ASR model. k: User specified encryption key to use while saving/loading the modelĪny overrides to the spec file. For the best experience, we recommend using an A100 GPU.įor training an ASR Citrinet model in TAO, we use the tao speech_to_text_citrinet train command with the following arguments: The following steps may take a considerable amount of time depending on the GPU being used. Sample commands are given below.Ī list of some of the customizable parameters along with their default values is as follows: The process of opening the training script, finding the parameters of interest (which might be spread across multiple files), and making the changes needed, is being replaced by a simple command-line interface.įor example, if the number of epochs are needed to be modified along with a change in the learning rate, you can add trainer.max_epochs=10 and optim.lr=0.02 and train the model. The TAO interface enables you to configure the training parameters from the command-line interface. Manifests=$DATA_DIR/an4_converted/train_manifest.json \ r $RESULTS_DIR/citrinet/create_tokenizer \ e $SPECS_DIR/citrinet/create_tokenizer.yaml \ !tao speech_to_text_citrinet create_tokenizer \ Virtual Assistant (with Google Dialogflow)
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How to deploy custom Acoustic Model (Citrinet) trained with TAO Toolkit on Riva How to Fine-Tune a Riva ASR Acoustic Model (Citrinet) with TAO Toolkit How to pretrain a Riva ASR Language Modeling (n-gram) with TAO Toolkit How to Customize Riva ASR Vocabulary and Pronunciation with Lexicon Mapping How do I boost specific words at runtime with word boosting? How to Improve Recognition of Specific Words A language model can then be fine-tuned on that dataset to make it elicit strong and diverse question-answering skills.How do I use Riva ASR APIs with out-of-the-box models? Stack exchange instruction is a dataset that was obtained by scrapping the site in order to build a collection of Q&A pairs. Those answers are scored and ranked based on their quality. It is a place where a user can ask a question and obtain answers from other users. Stack Exchange is a well-known network of Q&A websites on topics in diverse fields.
To fine-tune cheaply and efficiently, we use Hugging Face 🤗's PEFT as well as Tim Dettmers' bitsandbytes. Unfortunately such datasets are not ubiquitous but thanks to Hugging Face 🤗's datasets library we can have access to some good proxies. That procedure requires the availability of quality instruction datasets, which contain multiple instruction - answer pairs. Instruction fine-tuning has gained a lot of attention recently as it proposes a simple framework that teaches language models to align their outputs with human needs. Our interest here is to fine-tune StarCoder in order to make it follow instructions. 💫 StarCoder can be fine-tuned to achieve multiple downstream tasks. Now that everything is done, you can clone the repository and get into the corresponding directory.
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