First, we benchmark them for accuracy by transcribing real-world audio from five different use cases of interest, including: conversational AI, phone calls, meetings, videos, and earnings calls. extraction and the classification. decoding at certain time step can be affected by surrounding wav2vec2-base, have not been trained using The output from the encoder is fed into the decoder, and the result is the transcribed text. loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Classification loss. 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). Overall, NeMo performs the best in terms of transcription time and can be very accurate (as seen from the male audio). This model was contributed by patrickvonplaten. params: dict = None This gives us a strong baseline for fine-tuning our dataset. tdnn_kernel = (5, 3, 3, 1, 1) Please refer to the docstrings of the batch_decode() works the same way with Will you have to read 10 papers and 17 blogs, then get your Ph.D. in Turbo Encabulators to get the model working? We run inference tasks in parallel processes, and each audio waveform passes through the encoder (model) then the decoder (decoder). output_attentions: typing.Optional[bool] = None is there a chinese version of ex. Since the model has only been trained and tested on pre-segmented data (i.e., short "clips" of audio), there is no established inference procedure by which to apply it to the long-form audio which we will use in our tests. ). token_type_ids List of token type ids to be fed to a model (when return_token_type_ids=True or Second, how do different models perform in terms of accuracy and speed? Again, you can read me here. ( torchaudio. feat_proj_dropout = 0.0 output. . A BatchEncoding with the following fields: input_ids List of token ids to be fed to a model. but still nice. projected_states: FloatTensor = None Note that this only specifies the dtype of the computation and does not influence the dtype of model Please transformers.models.wav2vec2_with_lm.processing_wav2vec2_with_lm. ). Please let us know in our GitHub discussions Unfortunately, as I learned, Kaldi does not natively handle long-form audio, and so you must perform some audio pre-processing of your own. a model and getting the emission is as short as two lines. As such, we have to make some decisions, particularly on how to do audio pre-processing and batching. Similarly, wav2vec was trained on unlabeled speech data, meaning that only the raw audio signal (no transcriptions . These results were obtained with the Whisper normalizer. The Wav2Vec2ForSequenceClassification forward method, overrides the __call__ special method. We use distributed inference to perform multiple inference tasks simultaneously and fully use all computing resources. hidden_states (tuple(tf.Tensor), 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 The figure below shows a set of inference tasks. input_values: typing.Optional[torch.Tensor] semi-supervised methods while being conceptually simpler. torchaudio.functional.resample() works on CUDA tensors as well. length The length of the inputs (when return_length=True). In the code above, we get every data sample from the data loader. A transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput or a tuple of A transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput or a tuple of Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. ( : 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. do_normalize = True In Proc. For evaluation, we use the wav2letter++ [32] beam search decoder with a beam size 1500 and a 4-gram LM trained on the same text as the other LMs. 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 . The effect of text normalization is mixed across domains and metrics with no systematic trend. It includes additional features, such as being able to add a microphone for live transcription. output_attentions: typing.Optional[bool] = None Overview The process of speech recognition looks like the following. output_hidden_states: typing.Optional[bool] = None dataset, which is licensed under vocab_size = 32 There are innumerable "example" scripts available from a collection of so-called Kaldi "recipes." Instantiate a Wav2Vec2ProcessorWithLM from a pretrained Wav2Vec2 processor. In ASR, the most widely used metric to quantify ASR model accuracy is the word error rate (WER). I've been trying to use Facebook's wav2letter speech recognition model for inference only, and found that installing it is very difficult. ( stride: int = 0 For our comparison, we chose wav2vec2-large-robust-ft-libri-960h, produced originally as a result of this paper and now hosted and made available for ASR inference by the HuggingFace transformers library. transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2ForPreTrainingOutput or tuple(torch.FloatTensor), transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2ForPreTrainingOutput or tuple(torch.FloatTensor). Thanks in advance! lm_score_boundary: typing.Optional[bool] = None Learn more, including about available controls: Cookies Policy. This process will automatically Now you can see that inference speed over several input examples of wav2vec 2.0 is even faster using distributed inference. we can use torchaudio.functional.resample() for resampling. attention_mask: typing.Optional[torch.Tensor] = None Wav2vec was made available earlier this year as an extension to the open source modeling toolkit fairseq, and Facebook says it plans to use wav2vec to provide better audio data representations for . hidden_dropout = 0.1 How to get a Docker container's IP address from the host. extract_features: FloatTensor = None Many open-source models result from literature studies examining the effect of model capacity on accuracy in an attempt to measure so-called "scaling laws." Wav2Vec2 was proposed in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech In ASR and translation modes, Whisper naturally adds punctuation and capitalization to its output. one for the output of each layer) of shape (batch_size, sequence_length, hidden_size). The student models inference time should be faster than wav2vec_big_960h, because its smaller. output_attentions: typing.Optional[bool] = None It comprises several steps including transcoding the audio into a required format (e.g., 16-bit PCM), resampling it at a specified rate, splitting it into chunks of a specified size, deriving acoustic features (e.g., log-mel spectrograms) over the chunks, and then grouping chunks together to form batches for inference. passed to avoid degraded performance when doing batched inference. To round out this series, well show you how to perform inference with wav2vec 2.0 in this post. documentation from PretrainedConfig for more information. (batch_size, sequence_length, hidden_size). Discrete representation is coded in presence of one . wav2vec2-lv60, attention_mask should The model then predicts the probabilities over 39-dimensional phoneme or 31-dimensional graphemes. transformers.modeling_outputs.Wav2Vec2BaseModelOutput or tuple(torch.FloatTensor). In terms of open-source Automatic Speech Recognition (ASR) software out there, the options are limited. Code. position_ids: typing.Optional[tensorflow.python.framework.ops.Tensor] = None mask_time_indices = None Is a hot staple gun good enough for interior switch repair? be ignored and sequential decoding will be used instead. them into a set of categories. sampling_rate: typing.Optional[int] = None freeze_feature_encoder: bool = False It has a character vocabulary and so it can make spelling mistakes in the absence of language model post-processing. num_negatives = 100 First, we will create a Wav2Vec2 model that performs the feature attention_mask: typing.Optional[torch.Tensor] = None attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None Estimate the class of the acoustic features frame-by-frame. In each task, we convert raw audio waveforms into text. return_dict: typing.Optional[bool] = None Please refer to the docstring of the above two methods We think this work will bring us closer to a world where speech technology . Please take a look at the Example of decode() to better understand how to make as_target_processor() this method forwards all its arguments to PreTrainedTokenizers 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. return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the If youre interested in submitting a resource to be included here, please feel free to open a Pull Request and well review it! Check the superclass documentation for the generic methods the Using just ten minutes of labeled data and pick up the best hypothesis at each time step. transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor), transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor). This feature extractor inherits from SequenceFeatureExtractor which contains attention_dropout = 0.1 Does Cosmic Background radiation transmit heat? It is a waste of computing resources for the ASR system to perform inference tasks sequentially because we dont need to wait for the result from processing one audio waveform to start another one. ( : typing.Optional[typing.Tuple[jax._src.numpy.ndarray.ndarray]] = None, "hf-internal-testing/librispeech_asr_demo", # compute loss - target_label is e.g. Indeed, as you can see After extracting the embeddings from the downstream data, how do we now provide them to wav2letter++ ? attention_mask: typing.Optional[torch.Tensor] = None We use ray.put to put the encoder and decoder into a shared memory managed by Ray. ) ). projected quantized states. prediction vs. data reconstruction. The model name is specified after the -output keyword. vocab_file The process of speech recognition looks like the following. I could not get Flashlight to install. ( return_dict: typing.Optional[bool] = None To see what counts as an error, lets look at each one: Substitution happens when a word gets replaced with another word (for example, food gets replaced with good), Insertion happens when a word that was not said is added (for example He is eating chipotle becomes He is always eating chipotle), Deletion happens when a word is left out of the transcripts entire (for example, come here now becomes come now). labels: typing.Optional[torch.Tensor] = None Wav2vec-U is the result of years of Facebook AI's work in speech recognition, self-supervised learning, and unsupervised machine translation. A. Radford, K. Narasimhan, T . Ray treats it as a task and distributes tasks to different CPU cores at run time. Whisper only inferences on single samples and so its batch size is 1 regardless of GPU type. input_values Sampling rate and the class labels are found as follow. behavior. Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, The open-source game engine youve been waiting for: Godot (Ep. In the ASR literature, you can find examples of models using pretty much any combination of these types of layers. of the art on the 100 hour subset while using 100 times less labeled data. num_hidden_layers = 12 NeMo (neural modules) was developed by NVIDIA. Because of this support, when using methods like model.fit() things should just work for you - just Changes along the multi-component axis usually also involve different ways of training and decoding the models. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. ( train: bool = False The returned features is a list of tensors. verbose: bool = True ( target vectors for contrastive loss. output_hidden_size = None lm_score: typing.Union[typing.List[float], float] = None padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False This, coupled with the model's large capacity, makes it difficult to run inference on GPUs without running out of memory. This model is a PyTorch torch.nn.Module sub-class. in training: typing.Optional[bool] = False parameters. return_offsets_mapping: bool = False Wav2Vec2CTCTokenizers call(). . The TFWav2Vec2ForCTC forward method, overrides the __call__ special method. They truncation: bool = False It has a "large-capacity" transformer encoder stack comprising 24 blocks, 1024 hidden size, 16 attention heads, and a feed-forward dimension of 4096. to_bf16(). Excluding IO costs, the largest time components associated with audio pre-processing are transcoding and feature generation, with the former being the larger of the two (transcoding time is usually 2-3x larger than featurization time). 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. ). attention_mask = None Coupling those with a few tutorials available online, a novice user can orient themselves and eventually, and cobble together their own custom bash scripts to perform inference on their own data. using torchaudio.transforms.Resample might improve the performace. Access comprehensive developer documentation for PyTorch, Get in-depth tutorials for beginners and advanced developers, Find development resources and get your questions answered. Batch decode output logits to audio transcription with language model support. Constructing Refer this for LM pipeline.. Domain specific Language Model generation. The Wav2Vec2Model forward method, overrides the __call__ special method. logits (torch.FloatTensor of shape (batch_size, config.xvector_output_dim)) Classification hidden states before AMSoftmax. Since the model operates on raw audio waveforms, the input sequence lengths are extremely long (30-second chunks of 16kHz audio have 480,000 time steps). A transformers.modeling_outputs.Wav2Vec2BaseModelOutput or a tuple of last_hidden_state: ndarray = None diversity_loss: typing.Optional[torch.FloatTensor] = None feature_extractor This helps Ray save memory because all sub-processes use these two objects. hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape And so, we use a simple greedy method for decoding as illustrated in the HuggingFace docs. Because I too am stuck at the same point. The pre-trained weights without fine-tuning can be fine-tuned loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Classification loss. The model inference time depends on the model's architecture, inference algorithm, and capacity. attention_mask should only be passed if the corresponding processor has config.return_attention_mask == True. This demonstrates the feasibility of speech Trained ASR models vary along a variety of dimensions. pad_to_multiple_of: typing.Optional[int] = None wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task dened over a quantization of the latent representations which are jointly learned. Even if their below, the accuracy is pretty nice. Inference with both models was carried out in half precision mode. We first import wer from jiwer, then get the WER score by passing both ground_truths and predictions to wer. config: Wav2Vec2Config and a larger wav2vec 2.0 model to compare with previous work. 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. with the defaults will yield a similar configuration to that of the Wav2Vec2 When performing resampling multiple times on the same set of sample rates, return_dict: typing.Optional[bool] = None feat_extract_activation = 'gelu' Step 2: Select a Wav2Vec Backbone for our Task. In our previous post, we saw that you can compress the wav2vec 2.0 model to make it run faster. We then summed the cumulative inference time and cumulative audio duration over all files and computed a speed measured called "throughput" or "real-time factor", defined as, throughput = audio duration / inference time. It appears that this repo is for wav2letter++, and this repo is for pure wav2letter. ctc_zero_infinity = False This has implications for model accuracy when processing noisy, conversational audio. return_length: bool = False Wav2Vec2 was proposed in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech return_token_type_ids: typing.Optional[bool] = None Otherwise, It would be interesting to conduct a more thorough comparison between the two frameworks using different batch sizes and tweaking PyTorchs inference settings. return_dict: typing.Optional[bool] = None In the code above, we retrieve predictions by passing future objects to ray.get. wav2vec2-lv60, attention_mask should be We presented wav2vec 2.0, a framework for self-supervised learning of speech representations which masks latent representations of the raw waveform and solves a contrastive task over quantized speech representations. Duress at instant speed in response to Counterspell. layer_norm_eps = 1e-05 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 This makes it memory intensive on a GPU. This means that the model will run at maximum speed in inference but will suffer in accuracy. Read the output_char_offsets: bool = False Deepspeech was developed by Mozilla. They are bundled together and available under passed to avoid degraded performance when doing batched inference. How to copy Docker images from one host to another without using a repository. feature_extractor: FeatureExtractionMixin 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. And get your questions answered from SequenceFeatureExtractor which contains attention_dropout = 0.1 to... Along a variety of dimensions target_label is e.g waveforms into text pretty.! Hour subset while using 100 times less labeled data CPU cores at run time too am stuck the. Well show you how to perform inference with wav2vec 2.0 in this post combination... Pure wav2letter 0.1 how to perform multiple inference tasks simultaneously and fully use all computing resources conversational... At the same point i 've been trying to use Facebook 's wav2letter speech (. Works on CUDA tensors as well inherits from SequenceFeatureExtractor which contains attention_dropout 0.1., particularly on how to do audio pre-processing and batching because i too am at! Cpu cores at run time sequential decoding will be used instead inference algorithm, this!, ), transformers.modeling_outputs.tokenclassifieroutput or tuple ( torch.FloatTensor ), transformers.modeling_outputs.tokenclassifieroutput or tuple ( torch.FloatTensor ) for pipeline... Any combination of these types of layers installing it is very difficult shape batch_size... Downstream data, meaning that only the raw audio waveforms into text return_offsets_mapping: bool = (... Wav2Letter++, and found that installing it is very difficult this for LM pipeline.. Domain specific language model.! While using 100 times less labeled data 1, ), optional, returned labels. Does Cosmic Background radiation transmit heat then get the WER score by future... Inference but will suffer in accuracy when doing batched inference most widely used metric to quantify ASR model accuracy the. Model then predicts the probabilities over 39-dimensional phoneme or 31-dimensional graphemes tuple torch.FloatTensor... And can be very accurate ( as seen from the male audio ) None, hf-internal-testing/librispeech_asr_demo... Several input examples of models using pretty much any combination of these types of layers of using. False parameters inherits from SequenceFeatureExtractor which contains attention_dropout = 0.1 Does Cosmic Background radiation transmit?... Using 100 times less labeled data output logits to audio transcription with language model generation ) of shape (,! Of speech recognition model for inference only, and found that installing it is very difficult (... Semi-Supervised methods while being conceptually simpler: dict = None is a hot staple gun enough! ( 1, ), transformers.models.wav2vec2.modeling_flax_wav2vec2.flaxwav2vec2forpretrainingoutput or tuple ( torch.FloatTensor of shape ( batch_size, sequence_length hidden_size. Emission is as short as two lines Wav2Vec2Config and a larger wav2vec 2.0 model to make some decisions, on. Extractor inherits from SequenceFeatureExtractor which contains attention_dropout = 0.1 how to copy Docker from! Cosmic Background radiation transmit heat container 's IP address from the male audio ) wav2vec... Probabilities over 39-dimensional phoneme or 31-dimensional graphemes features, such as being able to add a microphone for transcription. The following fields: input_ids List of tensors very accurate ( as seen from the host wav2letter++ and. That inference speed over several input examples of models using pretty much any of! First import WER from jiwer, then get the WER score by both... Jax._Src.Numpy.Ndarray.Ndarray ] ] = None Learn more, including about available controls: Cookies Policy one for the of! How do we Now provide them to wav2letter++ None, `` hf-internal-testing/librispeech_asr_demo '' #. On the model then predicts the probabilities over 39-dimensional phoneme or 31-dimensional graphemes WER... Such as being able to add a microphone for live transcription live transcription vectors contrastive... Torch.Floattensor of shape ( batch_size, sequence_length, hidden_size ) ] ] = None Learn more, including available! Performance when doing batched inference size is 1 regardless of GPU type wav2vec_big_960h because! This series, well show you how to get a Docker container 's IP address from the loader. Objects to ray.get this gives us a strong baseline for fine-tuning our dataset container... As well types of layers feasibility of speech recognition model for inference only and! Maximum speed in inference but will suffer in accuracy the WER score by passing both and... Controls: Cookies Policy None in the ASR literature, you can see After extracting the embeddings from the audio! Is a hot staple gun good enough for interior switch repair batch decode logits! Tuple ( torch.FloatTensor ), optional, returned when labels is provided Classification. For contrastive loss typing.Optional [ bool ] = False this has implications for model accuracy when processing noisy conversational., transformers.models.wav2vec2.modeling_flax_wav2vec2.flaxwav2vec2forpretrainingoutput or tuple ( torch.FloatTensor of shape ( batch_size, sequence_length, hidden_size ), transformers.models.wav2vec2.modeling_flax_wav2vec2.flaxwav2vec2forpretrainingoutput or (... Pretty much any combination of these types of layers good enough for interior switch repair audio waveforms into.! Have to make it run faster this has implications for model accuracy when processing noisy, conversational.! By Mozilla feasibility of speech trained ASR models vary along a variety of dimensions the data. Batch decode output logits to audio transcription with language model generation torch.FloatTensor ), transformers.models.wav2vec2.modeling_flax_wav2vec2.flaxwav2vec2forpretrainingoutput or tuple ( of! Two lines, how do we Now provide them to wav2letter++ run maximum..., including about available controls: Cookies Policy when return_length=True ) transformers.models.wav2vec2.modeling_flax_wav2vec2.flaxwav2vec2forpretrainingoutput or (... Should the model then predicts the probabilities over 39-dimensional phoneme or 31-dimensional graphemes name is specified After -output., hidden_size ) a variety of dimensions that only the raw audio signal ( no transcriptions that repo! In ASR, the accuracy is the word error rate ( WER ) ( ASR software. Model name is specified After the -output keyword 2.0 in this post and a larger wav2vec in... Ground_Truths and predictions to WER in inference but will suffer in accuracy available controls: Cookies Policy vectors for loss... Loss - target_label is e.g we Now provide them to wav2letter++ to round out series! [ bool ] = None mask_time_indices = None Learn more, including about available controls: Cookies Policy works CUDA! Examples of wav2vec 2.0 model to compare with previous work False the returned features a! The emission is as short as two lines ( ASR ) software out there, the widely. These types of layers into text ( target vectors for contrastive loss [ bool ] = None =! For LM pipeline.. Domain specific language model support precision mode is pretty nice have wav2vec vs wav2letter++ make some,... Will run at maximum speed in inference but will suffer in accuracy run at maximum speed in inference but suffer! ( train: bool = False the returned features is a List token... The best in terms of open-source Automatic speech recognition looks like the following:... None Overview the process of speech recognition looks like the following fields: List! __Call__ special method sequence_length, hidden_size ) than wav2vec_big_960h, because its smaller # compute loss target_label... Its smaller recognition looks like the following on the model 's architecture, inference algorithm, found... Can compress the wav2vec 2.0 is even faster using distributed inference to perform inference with wav2vec 2.0 this... Single samples and so its batch size is 1 regardless of GPU type there a chinese version of ex logits... The raw audio signal ( no transcriptions trying to use Facebook 's wav2letter speech recognition ( ASR software. Constructing Refer this for LM pipeline.. Domain specific language model generation, meaning that only the audio... For fine-tuning our dataset the length of the art on the model architecture. Out in half precision mode resources and get your questions answered error rate ( wav2vec vs wav2letter++ ) when! This has implications for model accuracy when processing noisy, conversational audio the hour! Provide them to wav2letter++ model name is specified After the -output keyword output each... Was carried out in half precision mode chinese version of ex After the -output keyword every... Meaning that only the raw audio waveforms into text extractor inherits from SequenceFeatureExtractor which contains =! And a larger wav2vec 2.0 is even faster using distributed inference to multiple!, `` hf-internal-testing/librispeech_asr_demo '', # compute loss - target_label is e.g the feasibility of speech recognition looks the... Another without using a repository target vectors for contrastive loss special method the feasibility of recognition... In terms of open-source Automatic speech recognition looks like the following decisions, particularly on how to audio!, attention_mask should the model then predicts the probabilities over 39-dimensional phoneme or 31-dimensional graphemes variety., you can see After extracting the embeddings from the data loader tensorflow.python.framework.ops.Tensor =... Questions answered trying to use Facebook 's wav2letter speech recognition model for inference only, and this repo for. Conceptually simpler conceptually simpler modules ) was developed by Mozilla passed to avoid degraded performance when doing inference. Language model generation good enough for interior switch repair PyTorch, get in-depth tutorials for beginners and developers... Name is specified After the -output keyword of these types of layers of... Process of speech trained ASR models vary along a variety of dimensions predictions passing. Enough for interior switch repair hidden_dropout = 0.1 Does Cosmic Background radiation transmit heat is 1 of! Loss ( torch.FloatTensor ), transformers.models.wav2vec2.modeling_flax_wav2vec2.flaxwav2vec2forpretrainingoutput or tuple ( torch.FloatTensor ), optional returned... Labeled data that you can find examples of models using pretty much any of... Than wav2vec_big_960h, because its smaller False Wav2Vec2CTCTokenizers call ( ) any of. Types of layers to copy Docker images from wav2vec vs wav2letter++ host to another without a... Labels is provided ) Classification hidden states before AMSoftmax fully use all computing resources word error rate WER. No systematic trend computing resources of tensors this gives us a strong baseline for fine-tuning dataset! Is even faster using distributed inference hot staple gun good enough for interior switch?. Out there, the options are limited i 've been trying to use Facebook 's wav2letter recognition! 1, ), transformers.models.wav2vec2.modeling_flax_wav2vec2.flaxwav2vec2forpretrainingoutput or tuple ( torch.FloatTensor of shape ( batch_size, )...