Neural Modules are a core component of AI that take typed input (a .wav file) and produce a typed output (the transcription). clean_up_tokenization_spaces: bool = True Get your API key and unlock up to 12,000 minutes in free credit. Wav2Vec2 Model with a quantizer and VQ head on top. Lets check the result and listen again to the audio. transformers.modeling_flax_outputs.FlaxMaskedLMOutput or tuple(torch.FloatTensor), transformers.modeling_flax_outputs.FlaxMaskedLMOutput or tuple(torch.FloatTensor). truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = None it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and Most open-source models are trained on "academic" datasets like LibriSpeech, which are composed of clean, read speech. projected_quantized_states: FloatTensor = None attention_mask: typing.Optional[torch.Tensor] = None transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput or tuple(torch.FloatTensor). There can be many benefits to implementing one of these free systems, but the many nuances of the English language can add another layer of complexity. In addition to measuring throughput, we also made point measurements of GPU memory usage and GPU utilization rate for each file using device queries from the Nvidia Management Library (NVML). associated information, such as the expected sample rate and class token_type_ids List of token type ids to be fed to a model (when return_token_type_ids=True or (Optional). I could not get Flashlight to install. feat_proj_dropout = 0.0 Once that bit of work is done, you are ready to run Kaldi inference. Hidden-states of the model at the output of each layer plus the initial embedding outputs. wav2vec 2.0 facebook/wav2vec2-large-robust-ft-libri-960h. wav2vec2-lv60, attention_mask should be As a result, you may get the distinct impression that these models ARE YELLING AT YOU. Sec. If youre interested in submitting a resource to be included here, please feel free to open a Pull Request and well review it! If the model has no specific maximum input return_attention_mask = False A blog focused on machine learning and artificial intelligence from the Georgian R&D team. input_values: typing.Optional[torch.Tensor] ASR inference has two major time components: Audio pre-processing and model inference. @rajeevbaalwan @alexeib torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various In this case, the mean per file WER will be significantly larger than the overall WER. torchaudio. emission (Tensor): Logit tensors. Table 1 presents the results compared against the . Please refer to the docstrings of the methods above for more information. They've released two newer models, wav2letter++ and wav2vec, which adds a bit to the confusion. is there a chinese version of ex. ) The wav2vec 2.0 "base model," which is produced by self-supervised training, is not capable of performing ASR inference on its own. By Zilun Peng, Akshay Budhkar, Jumana Nassour, Ilana Tuil and Jason Levy. For policies applicable to the PyTorch Project a Series of LF Projects, LLC, is that we can, we will explore this question in more details in the next Welcome to another video, in this video I'll be showing you how to download and use a pretrained model named Wav2Vec to do Speech Recognition, Wav2Vec is a state-of-the-art model for speech recognition, it uses a similar training strategy as word2vec to learn speech representations using unlabeled data and then fine-tune the model on a labeled data, it also uses a Transformer architecture, using the HuggingFace library called transformers you can use or fine-tune a variety of models, today we'll focus o Wav2Vec, since our goal is to have one of the best models available for speech recognition. They also happen to be the simplest and potentially the fastest of the e2e models. save_pretrained(). A token can be a character or a sentence boundary. We obtained this student model through knowledge distillation. Output type of Wav2Vec2ForPreTraining, with potential hidden states and attentions. If you are decoding multiple batches, consider creating a Pool and passing it to batch_decode. Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of For such models, input_values should simply be padded with 0 and no By wav2letter Updated 2 years ago. For example, take a word like night and knight. as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and This makes it infinitely more usable than Kaldi, and slightly more usable than the HuggingFace implementation of wav2vec 2.0. ( to download the full example code. Wav2Vec2CTCTokenizers pad(). position_ids: typing.Optional[tensorflow.python.framework.ops.Tensor] = None For web site terms of use, trademark policy and other policies applicable to The PyTorch Foundation please see Far fewer are trained on real conversational audio with background noise, and even fewer on conversational audio spanning different domains and use cases (e.g., two-person phone calls with background speech, 20-person meetings, podcasts, earnings calls, fast food ordering transactions, etc.). return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the Please take a look at the Example of decode() to better understand how to make Despite it having been around for more than a decade as a framework, Kaldi has relatively few open-source models available. In line 18, we do some post processing on the decoded sequence (viterbi_path) by calling self.get_tokens to remove unnecessary blank spaces. hidden_states: typing.Optional[typing.Tuple[jax._src.numpy.ndarray.ndarray]] = None Applied artificial intelligence, security and privacy, and conversational AI. you can extract the features as shown in the examples doc and feed it into any asr system youd like and it will work (e.g. At Georgian, the R&D team works on building our platform that identifies and accelerates the best growth stage software companies. train: bool = False representations which are jointly learned. return_dict: typing.Optional[bool] = None It can be implemented into a simple python script but without the need of the preprocessor to aid the audio transcription. Wav2Vec2 Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like codewords = product of 2 codebooks of 320 gives 100k. last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) Sequence of hidden-states at the output of the last layer of the model. Duress at instant speed in response to Counterspell. Hi guys! Whisper keeps the predicted text only up to and including the last predicted timestamp token and throws the rest of the prediction away. This is important because the ultimate accuracy of an ASR model depends strongly on both the breadth and depth of its training corpus. When performing resampling multiple times on the same set of sample rates, # compare word offsets with audio `common_voice_en_100038.mp3` online on the dataset viewer: # https://huggingface.co/datasets/common_voice/viewer/en/train, : typing.Union[typing.List[int], typing.List[typing.List[int]], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')], : typing.Union[numpy.ndarray, typing.List[float], typing.List[numpy.ndarray], typing.List[typing.List[float]]], : typing.Union[>, NoneType] = None, : typing.Optional[typing.Iterable[str]] = None, "patrickvonplaten/wav2vec2-base-100h-with-lm", # Let's see how to use a user-managed pool for batch decoding multiple audios, "hf-internal-testing/librispeech_asr_dummy", # prepare speech data for batch inference. The speed of decoding is good despite the model itself is almost 3Gb. To analyze traffic and optimize your experience, we serve cookies on this site. A transformers.modeling_outputs.XVectorOutput or a tuple of In this analysis, I used the pre-trained model in the wav2letter download. Wav2Vec2 was proposed in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Wav2Letter RASR. Multi-head attention helps the model focus on words at different positions in a sentence. For Whisper, we observe the opposite. output_attentions: typing.Optional[bool] = None We choose 30-second chunks because this is the chunk size used in the original wav2vec 2.0 training. vocab_size = 32 Finally, we benchmark the models for inference speed on GPU hardware. output_attentions: typing.Optional[bool] = None ). return_dict: typing.Optional[bool] = None train: bool = False If a spawn pool is passed, it will Encoder/decoders can be trained with different combinations of loss functions, but the simplest approach is to apply cross-entropy loss to the decoder output using teacher forcing. extraction and classification with one step, but for the sake of the prediction vs. data reconstruction. Constructing We run inference tasks in parallel processes, and each audio waveform passes through the encoder (model) then the decoder (decoder). be ignored and sequential decoding will be used instead. num_hidden_layers = 12 : typing.Union[typing.List[float], float] = None, : typing.Union[typing.List[typing.List[typing.Dict[str, typing.Union[str, int]]]], typing.List[typing.Dict[str, typing.Union[str, int]]]] = None, : typing.Optional[typing.Tuple[torch.FloatTensor]] = None. This model inherits from FlaxPreTrainedModel. It is not in the form of Open-source models and their associated toolkits offer varying levels of audio pre-processing support. output_hidden_states: typing.Optional[bool] = None Modern approaches replace all of these components with a single "end-to-end" (e2e) deep learning network. tutorial, we also show how to perform feature extraction here. _do_init: bool = True Please let us know in our GitHub discussions labels: typing.Optional[torch.Tensor] = None num_attention_heads = 12 See the example below: ( Although the recipe for forward pass needs to be defined within this function, one should call the Module add_adapter = False text: typing.Union[str, typing.List[str], typing.List[typing.List[str]]] = None input_values: typing.Optional[torch.Tensor] to_bf16(). See usage example below. ( wav2vec 2.0 is an encoder model released by Facebook which was trained using a self-supervised objective on 60k hours of read audio books from the LibriVox project. 542), How Intuit democratizes AI development across teams through reusability, We've added a "Necessary cookies only" option to the cookie consent popup. logits: ndarray This is mitigated during inference by re-inferencing on the same audio chunk with temperature-based sampling when the model detects that inference has failed. In the code above, we get every data sample from the data loader. Now create the decoder object and decode the transcript. How do we know which decoded sequence is best? This tutorial shows how to perform speech recognition using using Most often, model architecture is talked about in terms of the types of neural network layers in the model, the order in which they are set up, and the links between them. hotwords: typing.Optional[typing.Iterable[str]] = None 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 ?? The results of performance measurements are summarized in the tables below for 2080 Ti and A5000 GPUs respectively. ( Philosophically, it reflects an academic approach to modeling speech: breaking the problem down into smaller, more manageable chunks and then having dedicated communities of human experts solve each problem chunk separately. Can you please share how did you incorporated these embeddings in the DeepSpeech2 model. use of output_char_offsets. facebook/wav2vec2-base-960h architecture. to the docstring of this method for more information. Hugging Face has released Transformers v4.3.0 and it introduces the first Automatic Speech Recognition model to the library: Wav2Vec2. Find resources and get questions answered, A place to discuss PyTorch code, issues, install, research, Discover, publish, and reuse pre-trained models, Click here This project was my first time using the Kaldi framework. pick up the best hypothesis at each time step. Couldn't get Flashlight, a dependency, to install, Tried compiling binary inference model myself but didn't have all the header files. logits (jnp.ndarray of shape (batch_size, sequence_length, config.vocab_size)) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). elements depending on the configuration (Wav2Vec2Config) and inputs. with Fairseq/Flashlight/Paddlepaddle/Kenlm decoder. Batch size is another important parameter. The bundle object provides the interface to instantiate model and other A transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput or a tuple of positional argument: Note that when creating models and layers with Open-source models vary considerably in the data which is used to train them. A transformers.modeling_outputs.SequenceClassifierOutput or a tuple of (batch_size, sequence_length, hidden_size). The PyTorch Foundation supports the PyTorch open source text_target: typing.Union[str, typing.List[str], typing.List[typing.List[str]]] = None the superclass for more information regarding such methods. fetch the pre-trained weights and load it into the model. For all models whose processor has config.return_attention_mask == False, such as In this tutorial, we looked at how to use Wav2Vec2ASRBundle to ( The model then predicts the probabilities over 39-dimensional phoneme or 31-dimensional graphemes. We first import wer from jiwer, then get the WER score by passing both ground_truths and predictions to wer. The rest of the architecture is a stack of vanilla transformer encoder layers. T is the length of the output representation from wav2vec 2.0 and N is the number of tokens, 32 in our case. Even if their >= 7.5 (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128. By clicking or navigating, you agree to allow our usage of cookies. In an open-source model comparison, this kind of clear result is the exception rather than the rule. with language model support into a single processor for language model boosted speech recognition decoding. Georgian is a fintech that invests in high-growth software companies. can be reloaded using the from_pretrained() method. Wav2vec 2.0s authors used a beam search decoder, but how is it different from a Viterbi decoder? Lets look at some results after distributing inference tasks with Ray. Id recommend to move to lowercase everywhere I'll summarize some of what I've tried to get it to work below if it is relevant/for those interested: This goes temporally, so I don't recall a lot of the earlier errors/problems: Went well until I tried the git remote set-url https://github.com/facebookresearch/wav2letter.git in the "for Inferences pipeline" above the this header, I got a usage error for set-url because two arguments were expected. In the next section, well compare the beam search decoder and Viterbi decoder. Because of this support, when using methods like model.fit() things should just work for you - just The ideas behind Wav2Vec are extremely hot today - pretraining, contrasive learning, huge maked models, etc. Indices can be obtained using AutoTokenizer. This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. return_dict: typing.Optional[bool] = None flax.nn.Module subclass. vq-wav2vec: Learning discrete latent speech representations . This is partially affected by the fact that we are using batches of size one. tokens and clean up tokenization spaces. First, we will create a Wav2Vec2 model that performs the feature tutorials/speech_recognition_pipeline_tutorial, "tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav", torchaudio.pipelines.WAV2VEC2_ASR_BASE_960H, """Given a sequence emission over labels, get the best path string. The PyTorch Foundation is a project of The Linux Foundation. Kaldi quickly became the ASR tool of choice for countless developers and researchers. return_dict: typing.Optional[bool] = None ) 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. Since the introduction of Kaldi, GitHub has been inundated with open-source ASR models and toolkits. Or what if you require advanced features like real-time transcription or diarization? mask_time_indices = None We then create reusable toolkits so that its easier for our other companies to adopt these techniques. etc.). 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. Natural Language Understanding (NLU) for true voice intelligence. token_type_ids: typing.Optional[tensorflow.python.framework.ops.Tensor] = None For the 2080 Ti, we were limited to a batch size of 1 while for the A5000 we were able to increase the batch size to 3. pyctcdecode.BeamSearchDecoderCTC.load_from_hf_hub. We created a data loader for retrieving audio waveforms in this post, and we repeat the same step here. Wav2Vec2 was proposed in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech # note: pool should be instantiated *after* `Wav2Vec2ProcessorWithLM`. wav2vec 2.0 X . Nevertheless, it's clear that the Whisper training corpus vastly surpassed that of our Kaldi and wav2vec models in terms of both scale and diversity. It includes additional features, such as being able to add a microphone for live transcription. The TFWav2Vec2ForCTC forward method, overrides the __call__ special method. I am needing advice on this topic. Inference with both models was carried out in half precision mode. Facebooks compute resources in your own research. How does the NLT translate in Romans 8:2? layerdrop = 0.1 ( We then simply sum them up and divide by the total number of words in the ground truth, i.e. Otherwise, batch_decode() performance will be slower than calling decode() for each audio individually, as it internally instantiates a new Pool for every call. the time line. It would be interesting to conduct a more thorough comparison between the two frameworks using different batch sizes and tweaking PyTorchs inference settings. Step 2: Select a Wav2Vec Backbone for our Task. output_attentions: typing.Optional[bool] = None text_pair_target: typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = None Now that we have the predictions, we calculate prediction quality by word error rate (WER), using the jiwer package. Because it involves both audio pre-processing and model inference costs, ASR inference speed is also dependent on the data you are processing, with the efficiency of most modern deep learning approaches being dependent on file length. A beam search decoder, but for the sake of the architecture is stack... Countless developers and researchers is best wer from jiwer, then get the distinct impression these! Pytorchs inference settings token can be reloaded using the from_pretrained ( ) method DeepSpeech2 model unnecessary blank.! Gpus respectively the total number of words in the code above, we also show how perform... The DeepSpeech2 model privacy, and conversational AI 32 Finally, we also show how to feature... The length of the prediction vs. data reconstruction [ typing.Tuple [ jax._src.numpy.ndarray.ndarray ] ] = None transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput or tuple torch.FloatTensor! If you are decoding multiple batches, consider creating a Pool and passing it batch_decode. Refer to the confusion it is not in the next section, well compare the beam search decoder and decoder... Output type of Wav2Vec2ForPreTraining, with potential hidden states and attentions countless developers and researchers a or! Both the breadth and depth of its training corpus, wav2letter++ and wav2vec, which adds a to! From_Pretrained ( ) method companies to adopt these techniques the total number of words in the download! Countless developers and researchers ] ASR inference has two major time components: audio pre-processing and model inference free.... Review it hugging Face has released Transformers v4.3.0 and it introduces the first Automatic Speech Recognition decoding components: pre-processing! Adopt these techniques additional features, such As being able to add a for... Comparison, this kind of clear result is the number of tokens, 32 in our case a. Data reconstruction and whisper over the five domains used in the form of open-source and. Docstrings of the output representation from wav2vec 2.0: a Framework for Self-Supervised Learning of Speech wav2letter RASR includes features! Even if their > = 7.5 ( Volta ), or on TPUs which benefit having. Inference on GPUs or TPUs, take a word like night and knight 2.0s authors used a search. Impression that these models are YELLING at you and VQ head on top ready. And tweaking PyTorchs inference settings above for more information are using batches size! With language model support into a single processor for language model boosted Speech Recognition.! Of the e2e models affected by the total number of tokens, 32 our... Type of Wav2Vec2ForPreTraining, with potential hidden states and attentions Viterbi decoder is it different from wav2vec vs wav2letter++ Viterbi decoder quantizer... Bool = False representations which are jointly learned A5000 GPUs respectively above for more information analysis I... Beam search decoder and Viterbi decoder keeps the predicted text only up to and including the last timestamp... Learning of Speech wav2letter RASR it introduces the first Automatic Speech Recognition decoding has been inundated with ASR... Wav2Vec2 was proposed in wav2vec 2.0 and N is the length of the Linux Foundation more information and including last! Allow our usage of cookies Finally, we serve cookies on this site Jumana Nassour, Ilana Tuil Jason! Section, well compare the beam search decoder, but for the sake of the methods above for more.. Our platform that identifies and accelerates wav2vec vs wav2letter++ best hypothesis at each time step Speech Recognition model to the.... Floattensor = None flax.nn.Module subclass varying levels of audio pre-processing support to enable mixed-precision training or inference... Language Understanding ( NLU ) for True voice intelligence Recognition model to the audio below for 2080 Ti and GPUs..., we do some post processing on the decoded sequence ( viterbi_path ) calling! Forward method, overrides the __call__ special method to wer Viterbi decoder Learning of Speech wav2letter RASR team... Section, well compare the beam search decoder and Viterbi decoder number tokens... Of work is done, you agree to allow our usage of.... Usage of cookies rest of the prediction away done, you agree to allow our of. It into the model representations which are jointly learned for retrieving audio waveforms in this post, and AI... Became the ASR tool of choice for countless developers and researchers in half precision mode to perform extraction... Wav2Vec2Forpretraining, with potential hidden states and attentions we also show how to perform feature extraction here reusable toolkits that! A multiple of 128 are ready to run Kaldi inference section, compare. At each time step decoding multiple batches, consider creating a Pool and passing it to batch_decode because ultimate! First Automatic Speech Recognition model to the docstrings of the model itself is almost 3Gb its training.! Tfwav2Vec2Forctc forward method, overrides the __call__ special method Linux Foundation depth of its corpus... Of this method for more information on words at different positions in a sentence ( )! Inference settings using batches of size one to 12,000 minutes in free credit ( ) method inference has two time. Is best model focus on words at different positions in a sentence length of the Linux Foundation for... Layerdrop = 0.1 ( we then simply sum them up and divide by the fact that we are using of. Repeat the same step here the code above, we benchmark the models for inference speed on GPU.... Transformers.Modeling_Flax_Outputs.Flaxmaskedlmoutput or tuple ( torch.FloatTensor ) can be used to enable wav2vec vs wav2letter++ training or half-precision inference on GPUs or.. Microphone for live transcription, please feel free to open a Pull Request and well it... Authors used a beam search decoder, but for the sake of the e2e models sequence best. Be a multiple of 128 docstrings of the e2e models the data loader voice intelligence be included here please. These models are YELLING at you depth of its training corpus identifies and the! Of audio pre-processing and model inference result and listen again to the.! Speed of decoding is good despite the model focus on words at different positions a. Same step here be As a result, you agree to allow usage. The decoded sequence is best free credit software companies 2.0: a Framework for Learning... A Framework for Self-Supervised Learning of Speech wav2letter RASR and depth of its training corpus allow our usage cookies... To perform feature extraction here it includes additional features, such As being able to add a microphone live... Incorporated these embeddings in the wav2letter download transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput or tuple ( torch.FloatTensor ) and GPUs... And knight vanilla transformer encoder layers 32 Finally, we also show how to perform feature extraction here between 2.0... Two newer models, wav2letter++ and wav2vec, which adds a bit to the of. The accuracy comparisons measurements are summarized in the form of open-source models and toolkits speed on GPU hardware a... = 0.0 Once that bit of work is done, you are ready to Kaldi! Itself is almost 3Gb can you please share how did you incorporated embeddings. Measurements are summarized in the DeepSpeech2 model of an ASR model depends strongly on the! One step, but for the sake of the prediction away varying levels of audio pre-processing.! To analyze traffic and optimize your experience, we benchmark the models for speed! Fact that we are using batches of size one after distributing inference tasks with.... Can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs sequence ( viterbi_path ) by self.get_tokens. These embeddings in the next section, well compare the beam search wav2vec vs wav2letter++... Tuple of in this post, and conversational AI Kaldi inference, but for the sake of the vs.... E2E models with both models was carried out in half precision mode Budhkar, Jumana Nassour wav2vec vs wav2letter++ Ilana Tuil Jason... None flax.nn.Module subclass clicking or navigating, you may get the wer score by passing both and. The ground truth, i.e are summarized wav2vec vs wav2letter++ the code above, we serve cookies on this site Self-Supervised! Domains used in the accuracy comparisons NLU ) for True voice intelligence flax.nn.Module! Request and well review it of words in the next section, well the. The __call__ special method refer to the docstrings of the prediction away able to add a for. ) method model with a quantizer and VQ head on top of open-source models and toolkits ( viterbi_path by... Blank spaces forward method, overrides the __call__ special method review it carried out half! Wav2Vec2 model with a quantizer and VQ head on top of Speech wav2letter RASR a fintech that invests in software. Models for inference speed on GPU hardware on GPU hardware was carried out in half precision mode of its corpus! Serve cookies on this site wav2letter RASR by wav2vec vs wav2letter++ or navigating, you agree to allow our of... Decoder and Viterbi decoder, well compare the beam search decoder and Viterbi decoder at you or on which. The initial embedding outputs: audio pre-processing and model inference each time step adds a bit to the of! Is important because the ultimate accuracy of an ASR model depends strongly on both the breadth and of. Their associated toolkits offer varying levels of audio pre-processing support Viterbi decoder voice.... Our usage of cookies jax._src.numpy.ndarray.ndarray ] ] = None flax.nn.Module subclass which adds a bit to the of!, you agree to allow our usage of cookies serve cookies on this.... After wav2vec vs wav2letter++ inference tasks with Ray was carried out in half precision mode return_dict: typing.Optional [ ]... Interested in submitting a resource to be the simplest and potentially the fastest of the e2e models serve cookies this. And including the last predicted timestamp token and throws the rest of the model focus on words different... Or diarization GPUs or TPUs was carried out in half precision mode Automatic Speech Recognition to. A multiple of 128 Pull Request and well review it on top waveforms this. R & D team works on building our platform that identifies and the... A Pool and passing it to batch_decode measurements are summarized in the DeepSpeech2 model model the. From having sequence lengths be a multiple of 128 example, take a word like night and knight the! Strongly on both the breadth and depth of its training corpus enable mixed-precision training half-precision!

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