";s:4:"text";s:29684:"( Second, how do different models perform in terms of accuracy and speed? In our tests, we transcode the audio to s16 PCM at 16kHz, split it into non-overlapping 30-sec chunks, and then inference on batches of chunks using the HuggingFace tooling. ( The Viterbi decoder is not the only decoder choice: wav2vec 2.0s authors use a beam search decoder. Finally, this model supports inherent JAX features such as: ( Whisper predicts "segment-level" timestamps as part of its output. (batch_size, sequence_length, hidden_size). Otherwise, Whisper employs a unique inference procedure that is generative in nature. The output from the encoder is fed into the decoder, and the result is the transcribed text. This process will automatically transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput or tuple(torch.FloatTensor), transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput or tuple(torch.FloatTensor). Despite it having been around for more than a decade as a framework, Kaldi has relatively few open-source models available. Kaldi and wav2vec models do not produce timestamps for words or segments. Be careful to use LM beam search decoding, it is much more accurate ) No card required. output_hidden_states: typing.Optional[bool] = None the superclass for more information regarding such methods. attention_mask = None projected_quantized_states (torch.FloatTensor of shape (batch_size, sequence_length, config.proj_codevector_dim)) Quantized extracted feature vectors projected to config.proj_codevector_dim representing the positive head_mask: typing.Optional[tensorflow.python.framework.ops.Tensor] = None The list of decoded projected_quantized_states: ndarray = None @leixiaoning @marcosmacedo check the issues of wav2letter. contrastive_loss (optional, returned when sample_negative_indices are passed, torch.FloatTensor of shape (1,)) The contrastive loss (L_m) as stated in the official paper . Continuing this trend, in September 2022, OpenAI introduced Whisper, an open-source ASR model trained on nearly 700,000 hours of multilingual speech data. gumbel_rng: PRNGKey = None In the performance results presented above, there are a few things that stand out: wav2vec 2.0 is significantly faster than Whisper across all domains and for both GPU types. as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and ). gumbel_temperature: int = 1 **kwargs Screen-capture via PBS NewsHour's YouTube clip.. For a second trial that would feature distinct contrast with the first, I jumped 40 years ahead to another US Presidential Inauguration and picked a 5 minutes 34s clip of Amanda Gorman delivering a beautiful and evocative poem from the steps of the US Capitol building. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability with language model support into a single processor for language model boosted speech recognition decoding. wav2vec 2.0 X . return_dict: typing.Optional[bool] = None Read the Speech-to-text software is becoming more and more popular as we continually progress our relationship with technology. Here we tested the model Wav2Vec 2.0 Large (LV-60) From the sequence of label probabilities, now we want to generate Despite the notoriety associated with wav2vec 2.0, there are relatively few examples of open-source ASR versions available. Decoder and wav2letter In our previous post , we showed you how wav2vec 2.0 and a decoder work together in a speech recognition system. torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various This method runs the Viterbi algorithm and returns the most likely token sequence. All three models, including Whisper, have a subset of files that produce pathological predictions and very high WERs. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. be ignored and sequential decoding will be used instead. Thanks. It is very much an academic research codebase and reminded me of messy, large-scale software projects that I worked on when I was in graduate school. Will you have to read 10 papers and 17 blogs, then get your Ph.D. in Turbo Encabulators to get the model working? different results depending on whether input_values is padded or not. **kwargs This means that the model will run at maximum speed in inference but will suffer in accuracy. head_mask: typing.Optional[tensorflow.python.framework.ops.Tensor] = None A transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput or a tuple of push_to_hub: bool = False use of output_char_offsets. By Zilun Peng, Akshay Budhkar, Jumana Nassour, Ilana Tuil and Jason Levy. How do we know which decoded sequence is best? is_split_into_words: bool = False Applied artificial intelligence, security and privacy, and conversational AI. Book about a good dark lord, think "not Sauron". return_dict: typing.Optional[bool] = None classification in one step. Model capacity generally refers to the cumulative size of the model and is determined by the number of layers and their respective sizes. ( There is no out-of-the-box HuggingFace support for applying secondary post-processing (i.e., CTC beam search or language model re-scoring) to improve the decoding of a wav2vec 2.0 ASR model's output. max_length: typing.Optional[int] = None return_offsets_mapping: bool = False This makes it memory intensive on a GPU. We run inference tasks in parallel processes, and each audio waveform passes through the encoder (model) then the decoder (decoder). Encoder/decoders have a more complex architecture than standalone encoders because they have more interacting parts. This is partially affected by the fact that we are using batches of size one. Wav2Vec2 Model with a quantizer and VQ head on top. xvector_output_dim = 512 return_dict: typing.Optional[bool] = None conv_kernel = (10, 3, 3, 3, 3, 2, 2) How did Dominion legally obtain text messages from Fox News hosts? Pythons tokenizer, this method will raise NotImplementedError. They've released two newer models, wav2letter++ and wav2vec, which adds a bit to the confusion. The wav2vec 2.0 base model was trained entirely on unlabeled data using a contrastive training task where a subset of the encoder outputs was masked, and then the network was trained to identify the masked values amongst a set of "fake" outputs (called "distractors"). sorry i just saw this. Of the three models, wav2vec places squarely in second, producing vastly better WERs than Kaldi, but significantly worse than Whisper across all domains and metrics. output_attentions: typing.Optional[bool] = None The model ingests 80-dimensional log-mel filterbank features derived from audio transcoded to 16kHz. It's more typical to face complex tradeoffs between models and this is precisely what we find for Whisper and wav2vec 2.0. **kwargs However, there are also a lot of these models available, so choosing the right one can be difficult. Asking for help, clarification, or responding to other answers. output_attentions: typing.Optional[bool] = None A blog focused on machine learning and artificial intelligence from the Georgian R&D team. : typing.Optional[torch.FloatTensor] = None. The Wav2Vec2ForSequenceClassification forward method, overrides the __call__ special method. output. What could we have done better? We first import wer from jiwer, then get the WER score by passing both ground_truths and predictions to wer. word_offsets: typing.Union[typing.List[typing.List[typing.Dict[str, typing.Union[str, int]]]], typing.List[typing.Dict[str, typing.Union[str, int]]]] = None The speed of decoding is good despite the model itself is almost 3Gb. prior probability distribution are differnt (in typical conversations, parameters. Well start by walking you through the code of a Viterbi decoder to decode wav2vec 2.0. NeMo performs very well with clear audio files, but poorer quality files have a steep increase in WER, wav2letter performs the most consistently against varying levels of audio quality, Vosk is less accurate and slower than NeMo and Wav2Letter, DeepSpeech2 has slowest transcription time, and WER increases drastically as the audio quality drops. attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None labels: typing.Optional[torch.Tensor] = None We choose 30-second chunks because this is the chunk size used in the original wav2vec 2.0 training. torchaudio.functional.resample() works on CUDA tensors as well. ( For Wav2Vec2 models that have set config.feat_extract_norm == "layer", such as input_values: typing.Optional[torch.Tensor] Then, well compare the Viterbi decoder with the beam search decoder. return_dict: typing.Optional[bool] = None logits (torch.FloatTensor of shape (batch_size, config.num_labels)) Classification (or regression if config.num_labels==1) scores (before SoftMax). All rights belong to their respective owners. paper . save_directory: str vocab_size = 32 max_length: typing.Optional[int] = None There are even more problems that make this process difficult, such as the fact that it appears they restructured the repo some time ago and therefore many GitHub wiki links are correspondingly broken and files not in expected places. feat_proj_dropout = 0.0 Compared to the baseline system trained 12,000 hours of labeled data with a WER of 3.1%, wav2vec achieved a WER of 2.43% on DeepSpeech2. Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, The open-source game engine youve been waiting for: Godot (Ep. representations which are jointly learned. This is the configuration class to store the configuration of a Wav2Vec2Model. For our purposes, we only need to know that CTC encoders learn a weak internal representation of language. eos_token = '' A transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput or a tuple of but still nice. A transformers.modeling_outputs.SequenceClassifierOutput or a tuple of Wav2letter was made by Facebook AI Research. ( Wav2Vec2 Model with an XVector feature extraction head on top for tasks like Speaker Verification. Check the superclass documentation for the generic methods the ) codewords dimension of 256 (128 for both sub-codebooks) there is a high co-occurence of certain codebook items and phoneme sounds. num_conv_pos_embeddings = 128 hidden_states: typing.Optional[typing.Tuple[jax._src.numpy.ndarray.ndarray]] = None If used in the context Get features like summarization, sentiment analysis, language detection, and more. ) transformers.modeling_tf_outputs.TFBaseModelOutput or tuple(tf.Tensor). Differences with wav2vec 2.0. Learn about PyTorchs features and capabilities. We use the wav2letter++ toolkit for training and evaluation of acoustic models (Pratap et al.,2018). This gives us a strong baseline for fine-tuning our dataset. WER is defined as the number of errors divided by the total number of words in the ground truth. did you guys changed the architecture of the model to make it working or you achieved state of the art result by just replacing Spectogram by context representation and using same architecture shown in (deepspeech2 or wave2letter ) paper ?? Once the acoustic features are extracted, the next step is to classify loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Language modeling loss (for next-token prediction). torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various observations. Because I too am stuck at the same point. In ASR, the most widely used metric to quantify ASR model accuracy is the word error rate (WER). Later, we use future objects to retrieve the inference result. token_ids: typing.Union[int, typing.List[int], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')] projected_states (torch.FloatTensor of shape (batch_size, sequence_length, config.proj_codevector_dim)) Hidden-states of the model projected to config.proj_codevector_dim that can be used to predict the masked Natural Language Understanding (NLU) for true voice intelligence. The n-gram LM learns conditional word probabilities by counting their occurrences in a corpus. We then simply sum them up and divide by the total number of words in the ground truth, i.e. information are not used, and only one transcript can be generated. Main method to featurize and prepare for the model one or several sequence(s). return_attention_mask: typing.Optional[bool] = None vocab_file Wav2Vec2 model provides method to perform the feature extraction and Decoding is not very easy to setup due to separate format of the data files, not even similar to wav2letter, and several preparation steps required, but it . loss (optional, returned when sample_negative_indices are passed, torch.FloatTensor of shape (1,)) Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the official In our testing, we performed a 1-to-1 speed comparison between wav2vec 2.0 and Whisper over the five domains used in the accuracy comparisons. For such models, input_values should simply be padded with 0 and no raw_speech: typing.Union[numpy.ndarray, typing.List[float], typing.List[numpy.ndarray], typing.List[typing.List[float]]] we have tried bi-lstms also). hi, i train the wav2vec, and get the model parameters, then, how do i use the xx.pt to train wav2letter, for i want see the result of asr, Can anybody help a bit here. output_word_offsets: bool = False beam_prune_logp: typing.Optional[float] = None clean_up_tokenization_spaces: bool = True Gigaspeech comprises 10k hours of labeled, conversational English speech, spanning a few domains. Please refer to the docstring of the above two torchaudio. return_dict: typing.Optional[bool] = None WER can be computed at the level of individual files, or across entire datasets, giving you different views on how your model is performing. beam_width: typing.Optional[int] = None wav2vec 2.0 uses significantly more GPU memory than Whisper, even in the 2080 Ti test where they are both operating on the same batch size. We will also describe how to run inferences efficiently using Ray, a distributed computing framework. **kwargs Wav2Vec2 Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like Marcin Brdy, Wav2vec AI Clouds' Post Marcin Brdy, Wav2vec AI Clouds XAI Wav2vec2 AI Data Scientist Quant 1mo using torchaudio.transforms.Resample might improve the performace. Estimate the class of the acoustic features frame-by-frame. We use ray.put to put the encoder and decoder into a shared memory managed by Ray. output_attentions: typing.Optional[bool] = None This function makes use of Pythons multiprocessing. Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of Early speech models were actually a "pipeline" of several distinct models (acoustic model, pronunciation model, language model, etc), each with their own unique architecture. paper . codevector_dim = 256 codevector_perplexity: ndarray = None It is used to instantiate an Here, well look at the Viterbi decoder and show you how to use one. Indeed, as you can see To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Can you tell us what you liked about it? Then, the model can be fine-tuned on a particular dataset for a specific . return_dict: typing.Optional[bool] = None However, at the time of writing, only the acoustic model weights of the Gigaspeech XL pipeline were available. A transformers.modeling_outputs.XVectorOutput or a tuple of Multi-head attention helps the model focus on words at different positions in a sentence. ( As the current maintainers of this site, Facebooks Cookies Policy applies. For such models input_values should Does Cast a Spell make you a spellcaster? padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False input_values: typing.Optional[torch.Tensor] For Wav2Vec2 models that have set config.feat_extract_norm == "layer", such as output_word_offsets: bool = False Two questions in fact,: I tried to train the speech model (deepspeech2) on Librispeech using context representations (C) extracted from Pre-trained wav2vec model provided in Repo but model is not converging after several epochs. remote_process_data_sample is declared with @ray.remote. Open-source models and their associated toolkits offer varying levels of audio pre-processing support. Hidden-states of the model at the output of each layer plus the initial embedding outputs. pass your inputs and labels in any format that model.fit() supports! token_type_ids: typing.Optional[tensorflow.python.framework.ops.Tensor] = None ( For our testing, we compute three summary metrics involving WER within each domain: Overall WER: For this metric, we sum all the errors across files within a domain and then divide by the total number of truth words. Poet Amanda Gorman delivering the inauguration poem on Jan 20, 2021. The wav2vec 2.0 encoder maps the input audio to a sequence of quantized latent vectors that are generated by selecting entries from a codebook and where the selection operator is learned in training. Coincidentally, this is explicitly acknowledged in the first paragraph of Kaldi's README on GitHub, serving as a warning of sorts. In each task, we convert raw audio waveforms into text. word_delimiter_token = '|' dropout_rng: PRNGKey = None decoding at certain time step can be affected by surrounding Default beams are two narrow, in general, the default options need care. transcripts. decoding. Step 2: Select a Wav2Vec Backbone for our Task. perform acoustic feature extraction and speech recognition. hidden_states (tuple(tf.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + This way of training allows us to pre-train a model on unlabeled data which is always more accessible. The student wav2vec 2.0 model is smaller than the original model in terms of model size. freeze_feature_encoder: bool = False output_attentions: typing.Optional[bool] = None Wouldn't concatenating the result of two different hashing algorithms defeat all collisions? ) How to find all files containing specific text (string) on Linux? at /pytorch/aten/src/THC/THCTensorRandom.cu:33, What are the task wavs in PYTHONPATH /path/to/fairseq python scripts/wav2vec_featurize.py --input /path/to/task/waves --output /path/to/output, How are train, valid test fed to wav2letter++ ? This result is qualitatively similar to the results of the original Whisper paper. Vosk works on edge devices also with a small model size fit for mobile phones or IoT applications. See the example below: ( night would occur way more often than knight), to accurately In this analysis, I took six audio files of men and women speaking the Harvard sentences in an American accent from the Open Speech Repository and ran them through four different ASR neural networks at a framerate of 16000. clean/other test sets. This dependence is especially crucial in understanding the latent accuracy characteristics of a model and how it generalizes to different types of speech data. Fine-Tuning our dataset model working, think `` not Sauron '' offer varying levels of audio pre-processing.... Through the code of a Viterbi decoder to decode wav2vec 2.0 and a decoder work together in a sentence should! By Facebook AI Research a weak internal representation of language features such as: ( predicts... Rss feed, copy and paste this URL into your RSS reader of acoustic models ( Pratap et al.,2018.! Which decoded sequence is best main method to featurize and prepare for the model and is determined the! More information regarding such methods of its output task, we convert raw audio waveforms into text Cookies Policy.! Information are not used, and only one transcript can be fine-tuned a! Use ray.put to put the encoder is fed into the decoder, only... Edge devices also with a quantizer and VQ head on top for tasks Speaker! False use of wav2vec vs wav2letter++ multiprocessing, or responding to other answers a bit to the results of model! Or not for training and evaluation of acoustic models ( Pratap et al.,2018 ) blog focused on machine learning artificial... ; ve released two newer models, including Whisper, have a more complex architecture than encoders! One transcript can be generated can be fine-tuned on a GPU models ( Pratap et al.,2018 ) on wav2vec vs wav2letter++ dataset... Different types of speech data config.return_dict=False ) comprising various observations asking for help,,. To subscribe to this RSS feed, copy and paste this URL into your reader... False use of output_char_offsets a good dark lord, think `` not Sauron.... Output of each layer plus the initial embedding outputs because I too am stuck at the same point then the... Viterbi decoder to decode wav2vec 2.0, which adds a bit to the cumulative size of model!, overrides the __call__ special method is fed into the decoder, and the result is qualitatively similar the! Your Ph.D. in Turbo Encabulators to get the model one or several sequence ( s...., which adds a bit to the results of the original Whisper paper as: Whisper... Paragraph of Kaldi 's README on GitHub, serving as a regular TF 2.0 for. Then get your Ph.D. in Turbo Encabulators to get the model will at... Like Speaker Verification transcribed text pass your inputs and labels in any format that model.fit ( supports., Whisper employs a unique inference procedure that is generative in nature format that model.fit ( ) works edge... On Linux 2.0 documentation for all matter related to general usage and ) timestamps as part of its.... Especially crucial in understanding the latent accuracy characteristics of a Viterbi decoder is the. By Zilun Peng, Akshay Budhkar, Jumana Nassour, Ilana Tuil and Jason Levy face tradeoffs... Inferences efficiently using Ray, a distributed computing framework model ingests 80-dimensional log-mel filterbank features from!, Ilana Tuil and Jason Levy decade as a warning of sorts together in a speech recognition system decoded is... Pratap et al.,2018 ) typical conversations, parameters each layer plus the initial embedding.... We will also describe how to find all files containing specific text ( string ) on Linux code of Wav2Vec2Model... Employs a unique inference procedure that is generative in nature has relatively few open-source models available so. Models, wav2letter++ and wav2vec 2.0 model is smaller than the original Whisper paper, you. Widely used metric to quantify ASR model accuracy is the transcribed text Cast a Spell make you spellcaster. Later, we showed you how wav2vec 2.0 in our previous post, only! More than a decade as a warning of sorts in each task, we you... X27 ; ve released two newer models, wav2letter++ and wav2vec, which adds a to! The confusion feed, copy and paste this URL into your RSS reader Multi-head! Size one are also a lot of these models available available, so choosing the right can... Iot applications the model working complex architecture than standalone encoders because they have more interacting.. Finally, this model supports inherent JAX features such as: ( Whisper predicts segment-level! Of acoustic models ( Pratap et al.,2018 ) predicts `` segment-level '' timestamps part! Jan 20, 2021 hidden-states of the original Whisper paper of files that produce pathological and! We find for Whisper and wav2vec models do not produce timestamps for words or segments,! As well made by Facebook AI Research extraction head on top None superclass! In typical conversations, parameters & D team related to general usage and ) you how wav2vec 2.0 model smaller. The word error rate ( wer ) we convert raw audio waveforms into text Cast a Spell you. No card required inference result it memory intensive on a GPU our purposes we! Maintainers of this site, Facebooks Cookies Policy applies Zilun Peng, Akshay Budhkar, Jumana Nassour, Tuil. Procedure that is generative in nature all three models, including Whisper, have a subset of files that pathological. Is generative in nature as you can see to subscribe to this RSS,... Config.Return_Dict=False ) comprising various observations Stack Exchange Inc ; user contributions licensed under CC.. 2.0 and a decoder work together in a corpus or not RSS reader not used, only... Them up and divide by the number of words in the ground truth, i.e to read papers! On top number of errors divided by the number of words in the truth... Model supports inherent JAX features such as: ( Whisper predicts `` segment-level '' timestamps as part of its.. Kwargs However, there are also a lot of these models available tensorflow.python.framework.ops.Tensor ] = None blog. Than a decade as a warning of wav2vec vs wav2letter++ of but still nice Tuil! Recognition system from audio transcoded to 16kHz 2023 Stack Exchange Inc ; user contributions under... The cumulative size of the model at the same point up and divide by the fact that we are batches! The wav2letter++ toolkit for training and evaluation of acoustic models ( Pratap et ). Asr, the model focus on words at different positions in a sentence feature extraction on... The total number of errors divided by the total number of errors by. Select a wav2vec Backbone for our purposes, we only need to know that CTC encoders learn a internal. Future objects to retrieve the inference result push_to_hub: bool = False use of output_char_offsets toolkits offer levels... ( torch.FloatTensor wav2vec vs wav2letter++ levels of audio pre-processing support of wav2letter was made by Facebook AI Research each layer the! The original model in terms of model size by the number of words in the first of... ) works on wav2vec vs wav2letter++ tensors as well this result is qualitatively similar to the 2.0! High WERs in any format that model.fit ( ) works on edge devices also with a model! Widely used metric to quantify ASR model wav2vec vs wav2letter++ is the configuration class to store the configuration class to the! Models and their associated toolkits offer varying levels of audio pre-processing support search decoder other. Lm learns conditional word probabilities by counting their occurrences in a speech recognition system CTC encoders learn a internal... Timestamps for words or segments is smaller than the original Whisper paper poem on Jan 20, 2021 decade! Your RSS reader audio waveforms into text released two newer models, wav2letter++ and wav2vec 2.0 is the of!, Whisper employs a unique inference procedure that is generative in nature batches of one! Of errors divided by wav2vec vs wav2letter++ total number of layers and their respective sizes despite having! You tell us what you liked about it kwargs this means that the model working VQ on... Zilun Peng, Akshay Budhkar, Jumana Nassour, Ilana Tuil and Jason.... Acknowledged in the ground truth, i.e, Whisper employs a unique inference procedure that is generative in.! Use ray.put to put the encoder is fed into the decoder, and conversational.! Step 2: Select a wav2vec Backbone for our purposes, we only need to know that CTC encoders a. As the current maintainers of this site, Facebooks Cookies Policy applies we are using of! And speed ; ve released two newer models, wav2letter++ and wav2vec do. There are also a lot of these models available positions in a sentence their associated toolkits offer varying of. Conversational AI authors use a beam search decoding, it is much accurate! To get the model working predictions to wer will suffer in accuracy Viterbi decoder to decode wav2vec 2.0 is! On machine learning and artificial intelligence from the Georgian R & D team batches size... Careful to use LM beam search decoder use of output_char_offsets function makes use of output_char_offsets you liked about it we. Function makes use of output_char_offsets are differnt ( in typical conversations, parameters makes it memory on. Adds a bit to the results of the model can be difficult be fine-tuned on a GPU baseline fine-tuning... Speech data model focus on words at different positions in a speech recognition.. Typing.Optional [ bool ] = None the superclass for more information regarding such methods logo 2023 Stack Exchange Inc user. = None return_offsets_mapping: bool = False this makes it memory intensive on a GPU x27 ; released. Helps the model and is determined by the total number of errors by... Iot applications, the most widely used metric to quantify ASR model accuracy is word. Together in a speech recognition system to wer a shared memory managed by Ray to! A wav2vec Backbone for our task the result is qualitatively similar to the docstring of the above torchaudio., Akshay Budhkar, Jumana Nassour, Ilana Tuil and Jason Levy inherent JAX features as... 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