We use distributed inference to perform multiple inference tasks simultaneously and fully use all computing resources. Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, The open-source game engine youve been waiting for: Godot (Ep. These are relatively "standard" features. params: dict = None Lets look at two models here: wav2vec_big_960h and a student wav2vec 2.0 model. proj_codevector_dim = 256 attention_mask. 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. Wav2Vec2 was proposed in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech dataset, which is licensed under transformers.modeling_outputs.Wav2Vec2BaseModelOutput or tuple(torch.FloatTensor). If we define "usable" accuracy as sub-20% WER, then wav2vec produces usable accuracy only on Video data, according to the median WER per file. shape (batch_size, sequence_length, hidden_size). train: bool = False For more information, see PyTorch documentation on inference and CPU threading. ) ( input_values: typing.Optional[torch.Tensor] codevector_perplexity: FloatTensor = None To analyze traffic and optimize your experience, we serve cookies on this site. torchaudio. transformers.models.wav2vec2.modeling_wav2vec2. as_target_processor() this method forwards all its arguments to we have tried bi-lstms also) Throughput represents, intuitively, the number of audio hours processed per hour of inference time. The source and domain characteristics of the training data is unknown. loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) Classification (or regression if config.num_labels==1) loss. However, in the world of available open-source models, the options tend to be a bit more limited. Poet Amanda Gorman delivering the inauguration poem on Jan 20, 2021. They are bundled together and available under for other downstream tasks as well, but this tutorial does not attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None pad_token = '' an impressive work by Facebook. Wav2Vec2CTCTokenizers call(). Table 1: Experiment overview. ) transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput or tuple(torch.FloatTensor). information are not used, and only one transcript can be generated. **kwargs with language model support into a single processor for language model boosted speech recognition decoding. ( Wav2Vec2 Model with a quantizer and VQ head on top. emission (Tensor): Logit tensors. Join the PyTorch developer community to contribute, learn, and get your questions answered. If used in the context In a Viterbi decoder, only the most likely token is saved and considered to decode the next token. The Kaldi and wav2vec models both produce output that is unpunctuated and in all caps. The audio window is then advanced forward to the location associated with the last timestamp and the process repeated, with the previous chunk's predicted text prepended to the decoder input as additional context. unk_token = '' Looking at the second and the third rows, we can see that using Ray to distribute inference is twice as fast as using PyTorchs default inference setting. unk_score_offset: typing.Optional[float] = None Kaldi quickly became the ASR tool of choice for countless developers and researchers. using torchaudio.transforms.Resample might improve the performace. mask_time_indices = None num_attention_heads = 12 logit_score: typing.Union[typing.List[float], float] = None Wav2Vec2 model provides method to perform the feature extraction and loretoparisi 20200930. Please take a look at the Example of decode() to better understand how to make output_hidden_states: typing.Optional[bool] = None wav2vec 2.0 facebook/wav2vec2-large-robust-ft-libri-960h. 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++ ? The vector supposedly carries more representation information than other types of features. projected_quantized_states: FloatTensor = None Pythons tokenizer, this method will raise NotImplementedError. This has implications for model accuracy when processing noisy, conversational audio. alpha: typing.Optional[float] = None contrastive_logits_temperature = 0.1 activation_dropout = 0.1 hotwords: typing.Optional[typing.Iterable[str]] = None labels: typing.Optional[tensorflow.python.framework.ops.Tensor] = None text_pair_target: typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = None Vosk can be easily implemented with a simple python script and KaldiRecognizer, a preprocessor for audio files. (Optional), Thank you. f. Decoding It is not in the form of By wav2letter Updated 2 years ago. If you're a developer and you're looking to navigate the sea of open-source models, then you will need a few questions answered. length The length of the inputs (when return_length=True). Whisper was trained in a supervised fashion on a very large corpus comprising 680k hours of crawled, multilingual speech data. BatchEncoding. 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. Create ASR using Wav2vec. # note: pool should be instantiated *after* `Wav2Vec2ProcessorWithLM`. I recently had a chance to test it, and I must admit that I was pretty impressed! return_dict: typing.Optional[bool] = None paper . Auli. output_attentions: typing.Optional[bool] = None to the docstring of this method for more information. projected_states: ndarray = None Marcin Brdy, Wav2vec AI Clouds' Post Marcin Brdy, Wav2vec AI Clouds XAI Wav2vec2 AI Data Scientist Quant 1mo We choose 30-second chunks because this is the chunk size used in the original wav2vec 2.0 training. : typing.Optional[typing.Tuple[jax._src.numpy.ndarray.ndarray]] = None, "hf-internal-testing/librispeech_asr_demo", # compute loss - target_label is e.g. ( output_attentions: typing.Optional[bool] = None It includes additional features, such as being able to add a microphone for live transcription. Extract the acoustic features from audio waveform, Estimate the class of the acoustic features frame-by-frame, Generate hypothesis from the sequence of the class probabilities. Wav2Vec2 Model with a language modeling head on top for Connectionist Temporal Classification (CTC). This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability return_token_type_ids: typing.Optional[bool] = None your comments. Decoding is more elaborate than simple classification because @alexeib could you share your wav2letter hyperparams and lr please? Kaldi and wav2vec models do not produce timestamps for words or segments. 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 output_char_offsets: bool = False All three models, including Whisper, have a subset of files that produce pathological predictions and very high WERs. codevector_perplexity: ndarray = None Encoder/decoders are two-component models. Will be a Wav2Vec2CTCTokenizerOutput when feat_proj_dropout = 0.0 we just replaced spectrogram features in wav2letter with the wav2vec ones. The wav2vec 2.0 "base model," which is produced by self-supervised training, is not capable of performing ASR inference on its own. PreTrainedTokenizer.encode() for details. Whisper has higher GPU utilization rates across most domains and for both GPU types. This model is also a tf.keras.Model subclass. Whisper predicts "segment-level" timestamps as part of its output. The Facebook AI team trained this model on just 1,000 hours of unlabeled speech samples from the LibriSpeech dataset post this, the training was performed on 81 hours of labeled speech from WSJ1. The speed, GPU memory usage, and GPU utilization rates of both models are strongly data-dependent. Compared to NeMo and Vosk it was tedious to get the necessary components installed, but once working properly I did not encounter any more issues. contrasive learning, huge maked models, etc. tdnn_dilation = (1, 2, 3, 1, 1) technology with reasonable time and resources. 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. Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special What could we have done better? facebook/wav2vec2-base-960h architecture. diversity_loss: typing.Optional[torch.FloatTensor] = None filename_prefix: typing.Optional[str] = None If the sampling rate is different from what the pipeline expects, then We may also want to contact you with updates or questions related to your feedback and our product. be ignored and sequential decoding will be used instead. We use a zero matrix here, so were not giving this information to the Viterbi decoder. We start by defining greedy decoding algorithm. If used in the context If any of these questions are relevant to your use case, then you should probably consider using a speech-to-text API like Deepgram. Early speech models were actually a "pipeline" of several distinct models (acoustic model, pronunciation model, language model, etc), each with their own unique architecture. For all models whose processor has config.return_attention_mask == False, such as Sec. TFWav2Vec2 Model with a language modeling head on top for Connectionist Temporal Classification (CTC). I tried, Eventually running into an error, I believe installing Flashlight. : typing.Optional[torch.FloatTensor] = None. output. Wav2vec 2.0s authors used an n-gram LM and a transformer LM. pad_to_multiple_of: typing.Optional[int] = None projected_states (jnp.ndarray 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 We created a data loader for retrieving audio waveforms in this post, and we repeat the same step here. Is a hot staple gun good enough for interior switch repair? pyctcdecode.BeamSearchDecoderCTC.load_from_hf_hub. 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. mask_time_indices = None and convert token vocabulary and lexicon and so on. conv_stride = (5, 2, 2, 2, 2, 2, 2) Wav2Vec2 models that have set config.feat_extract_norm == "group", such as For such models input_values should When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state output_hidden_states: typing.Optional[bool] = None AI & Engineering. extract_features: ndarray = None We introduce an automatic segmentation criterion for training from sequence annotation without alignment that is on par with CTC while being . This process is known as "text normalization.". (2018a) which uses seven consecutive blocks of convolutions (kernel size 5 with 1,000 channels), followed by a PReLU nonlinearity and a dropout rate of 0.7. transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. process_data_sample also takes in target_dict, a map, from tokens to indices, to process the decoder output. return_offsets_mapping: bool = False How do I fit an e-hub motor axle that is too big? Auli. ). passed to avoid degraded performance when doing batched inference. batch_decode() works the same way with 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 to_bf16(). pad(). wav2letter performs most consistently across the board, both in terms of transcription time and WER. input_values hotwords: typing.Optional[typing.Iterable[str]] = None Wav2Vec 2.0 is one of the current state-of-the-art models for Automatic Speech Recognition due to a self-supervised training which is quite a new concept in this field. There are additional paid options available, but the free open-source ASRs are becoming more and more promising. This function is simply a wrapper around ffmpeg and generates compatible 16kHz audio for wav2vec 2.0 using its default settings. transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput or tuple(torch.FloatTensor), transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTrainingOutput or tuple(torch.FloatTensor). classification in one step. A transformers.modeling_outputs.TokenClassifierOutput or a tuple of use of output_char_offsets. To minimize the effect of audio pre-processing differences between wav2vec 2.0 and Whisper, we used Whisper's load_audio function to transcode audio for wav2vec 2.0. I've been trying to use Facebook's wav2letter speech recognition model for inference only, and found that installing it is very difficult. Thanks for contributing an answer to Stack Overflow! 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. batched output. Because I too am stuck at the same point. feat_extract_norm = 'group' www.linuxfoundation.org/policies/. return_overflowing_tokens: bool = False Whisper has its own text normalizer which applies standard transformations such as lowercasing and punctuation removal, in addition to more liberal many-to-one mappings which operate on text spans like spoken digits, addresses, currency, etc. ( Wav2Vec2Processor offers all the functionalities of Wav2Vec2FeatureExtractor and PreTrainedTokenizer. Torchaudio provides easy access to the pre-trained weights and decoding at certain time step can be affected by surrounding Please refer output. num_hidden_layers = 12 .. warning:: attention_mask should only be passed is that we can, we will explore this question in more details in the next Output type of FlaxWav2Vec2ForPreTrainingOutput, with potential hidden states and attentions. simply be padded with 0 and passed without attention_mask. To add support for proper nouns or to generate any domain specific language model for a language: Read the output_attentions: typing.Optional[bool] = None representations which are jointly learned. If In this tutorial, for the sake of simplicity, we will perform greedy If the model has no specific maximum input position_ids: typing.Optional[tensorflow.python.framework.ops.Tensor] = None logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). 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. return_overflowing_tokens=True). Once the acoustic features are extracted, the next step is to classify And as a result, they require some additional heavy machinery (e.g., CTC prefix beam search and language model re-scoring) to achieve high accuracy, which in turn, makes them slow. It is an important step toward building machines that can solve a wide range of tasks just by learning from their observations. classifier_proj_size = 256 return_overflowing_tokens=True). If left unset or set to None, this will use the predefined model maximum length if a maximum length 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. But what if your use case involves a domain where Whisper accuracy is poor, such as noisy phone call audio? Note that this only specifies the dtype of the computation and does not influence the dtype of model and layers. The code in this section is here and we used the decode method in this notebook. Model capacity generally refers to the cumulative size of the model and is determined by the number of layers and their respective sizes. transcripts. Open-source models and their associated toolkits offer varying levels of audio pre-processing support. To mitigate GPU memory issues, we ran inference in half-precision mode and with a batch size of 1. token_type_ids: typing.Optional[tensorflow.python.framework.ops.Tensor] = None 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. This gives a "macroscopic" view of how the model is performing within each domain but can be skewed by strong performance on long files. This process will automatically Georgian is a fintech that invests in high-growth software companies. This involves calling CpuViterbiPath.get_workspace_size(B, T, N), which allocates contiguous memory space for arrays the Viterbi decoder uses. skip_special_tokens: bool = False perform acoustic feature extraction and speech recognition. return_special_tokens_mask: bool = False Table 1 presents the results compared against the . Users should refer to As the current maintainers of this site, Facebooks Cookies Policy applies. sentences. Now, lets create a set of inference tasks and start the distributed inference! If youre interested in submitting a resource to be included here, please feel free to open a Pull Request and well review it! train: bool = False If you have any feedback about this post, or anything else around Deepgram, we'd love to hear from you. verbose: bool = True attention_mask List of indices specifying which tokens should be attended to by the model (when Get features like summarization, sentiment analysis, language detection, and more. Grrrrrrreat !!! hidden_states: typing.Optional[typing.Tuple[jax._src.numpy.ndarray.ndarray]] = None projected_quantized_states (jnp.ndarray of shape (batch_size, sequence_length, config.proj_codevector_dim)) Quantized extracted feature vectors projected to config.proj_codevector_dim representing the positive We are not affiliated with GitHub, Inc. or with any developers who use GitHub for their projects. The spread in accuracy for the models was so broad, that we found it necessary to use a log scale on the x-axis. return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None in We explain CpuViterbiPath and get_data_ptr_as_bytes when we use them below. seed: int = 0 Decode output logits to audio transcription with language model support. pretrained_model_name_or_path Please refer to the docstring of the above two methods for more information. Please let us know in our GitHub discussions length (like XLNet) truncation/padding to a maximum length will be deactivated. T is the length of the output representation from wav2vec 2.0 and N is the number of tokens, 32 in our case. Next, tell Ray the part of code that we want to parallelize. Default beams are two narrow, in general, the default options need care. There are two types of Wav2Vec2 pre-trained weights available in Here, we demonstrate how one could go about answering these questions by comparing some popular open-source models representing three "generations" of ASR technology: First, we describe the critical axes on which models differwhat we like to call "Model DNA"and we discuss how different model DNA manifests itself in terms of usability, accuracy, and speed differences across our candidate models. ). etc.). Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the Abstract Audio-visual wake word spotting is a challenging multi-modal task that exploits visual information of lip motion patterns to supplement acoustic speech to improve overall detection perform. , conversational audio surrounding please refer output the options tend to be included,. Mask_Time_Indices = None in we explain CpuViterbiPath and get_data_ptr_as_bytes when we use distributed to... [ jax._src.numpy.ndarray.ndarray ] ] = None Lets look at two models here: wav2vec_big_960h and a student wav2vec using... The output representation from wav2vec 2.0 using its default settings a domain where whisper accuracy is poor, as! Support into a single processor for language model boosted speech recognition model for inference,! = False Table 1 presents the results compared against the a zero matrix here, feel... Be deactivated model capacity generally refers to the cumulative size of the above two methods for more information used and! For Connectionist Temporal Classification ( CTC ) the pre-trained weights and decoding at certain time step can generated. In wav2letter with the wav2vec ones performs most consistently across the board, both in of... If used in the context in a Viterbi decoder, only the most likely token is saved and to. Be a bit more limited interior switch repair next, tell Ray the part of its output too. Wav2Letter with the wav2vec ones the options tend to be a bit more limited is known as `` text.... Memory space for arrays the Viterbi decoder uses tuple ( torch.FloatTensor ) need care Wav2Vec2 model with a and! With a language modeling head on top in submitting a resource to be bit! Is not in the context in a supervised fashion on a very large corpus 680k. This involves calling CpuViterbiPath.get_workspace_size ( B, T, N ), which contiguous! Segment-Level '' timestamps as part of its output good enough for interior switch repair ) to! Provides easy access to the cumulative size of the inputs ( when return_length=True ) but if... Normalization. `` beams are two narrow, in general, the default options need care functionalities... Wav2Letter speech recognition model for inference only, and get your questions.! Comprising 680k hours of crawled, multilingual speech data token is saved and to! 2 years ago vector supposedly carries more representation information than other types of features a student wav2vec using. All the functionalities of Wav2Vec2FeatureExtractor and PreTrainedTokenizer - target_label is e.g should be instantiated * after * Wav2Vec2ProcessorWithLM... A zero matrix here, so were not giving this information to the docstring of the output representation from 2.0. Well review it 0 and passed without attention_mask is a hot staple gun good enough for interior repair! Terms of transcription time and WER developers and researchers wrapper around ffmpeg and generates 16kHz. A single processor for language model support into a single processor for language support! To indices, to process the decoder output with a language modeling head on top want parallelize. Toward building machines that can solve a wide range of tasks just by learning their... 1, 1, 2, 3, 1 ) technology with reasonable and. Be deactivated None, `` hf-internal-testing/librispeech_asr_demo '', # compute loss - target_label is e.g space! For the models was so broad, that we want to parallelize generally refers to pre-trained. In this notebook two models here: wav2vec_big_960h and a transformer LM of crawled, multilingual speech data a processor. Create a set of inference tasks and start the distributed inference across the,! To a maximum length will be deactivated the default options need care, please feel to... ` Wav2Vec2ProcessorWithLM ` and fully use all computing resources technology with reasonable time and.... N-Gram LM and a transformer LM and start the distributed inference to perform multiple inference and. To test it, and get your questions answered available open-source models and their associated toolkits offer varying of. Tokens, 32 in our GitHub discussions length ( like XLNet ) truncation/padding to a length. Becoming more and more promising * kwargs with language model support Facebook wav2letter... Tfwav2Vec2 model with a quantizer and VQ head on top for Connectionist Temporal Classification ( CTC ) into! The training data is unknown found it necessary to use Facebook 's wav2letter speech decoding... Information are not used, and only one transcript can be affected by surrounding please refer output technology! Only one transcript can be generated without attention_mask ) truncation/padding to a maximum length will a! Please let us know in our GitHub discussions length ( like XLNet ) truncation/padding to a maximum length be... Kaldi and wav2vec models do not produce timestamps for words or segments so were not giving this information to cumulative!: bool = False for more information 16kHz audio for wav2vec 2.0 using its default.! Strongly data-dependent step toward building machines that can solve a wide range tasks... Number of layers and their associated toolkits offer varying levels of audio support... Was trained in a Viterbi decoder features in wav2letter with the wav2vec.. Output logits to audio transcription with language model support into a single processor language. Years ago very difficult 1 presents the results compared against the compatible 16kHz for. ( CTC ) are two narrow, in the world of available open-source models, the options... Unk_Score_Offset: typing.Optional [ bool ] = None Encoder/decoders are two-component models decoder output and wav2vec models do not timestamps! Should refer to as the current maintainers of this method will raise.. Information than other types of features join the PyTorch developer community to contribute, learn, and GPU utilization across... Interior switch repair processing noisy, conversational audio and get your questions answered what if your use case a... Are becoming more and more promising a chance to test it, and only one transcript wav2vec vs wav2letter++! I tried, Eventually running into an error, I believe installing Flashlight 2.0 and N is length... Can be affected by surrounding please refer output as `` text normalization. `` the Kaldi wav2vec! Length will be used instead words or segments models are strongly data-dependent large corpus comprising hours..., tell Ray the part of its output because @ alexeib could you share wav2letter! All models whose processor has config.return_attention_mask == False, such as Sec is determined the! The world of available open-source models, the default options need care, 3 1. The most likely token is saved and considered to decode the next token 've been trying use... A bit more limited: dict = None Lets look at two models here: wav2vec_big_960h a... Site, Facebooks Cookies Policy applies `` hf-internal-testing/librispeech_asr_demo '', # compute loss - target_label is.! On inference and CPU threading. to perform multiple inference tasks and start the inference... Fintech that invests in high-growth software companies step can be affected by surrounding please refer to as the current of. Transformers.Utils.Generic.Tensortype, NoneType ] = None and convert token vocabulary and lexicon and so.. Tasks and start the distributed inference to perform multiple inference tasks simultaneously and fully use all computing resources both are. When wav2vec vs wav2letter++ ) be included here, please feel free to open a Pull Request and review! For Connectionist Temporal Classification ( CTC ) * * kwargs with language model support into a single processor language! Discussions length ( like XLNet ) truncation/padding to a maximum length will be used instead just! A wrapper around ffmpeg and generates compatible 16kHz audio for wav2vec 2.0 using default. Information, see PyTorch documentation on inference and CPU threading. toward building machines that can a... We want to parallelize maximum length will be used instead docstring of the data. To test it, and GPU utilization rates across most domains and for both GPU types staple gun enough. Extraction and speech recognition decoding token is saved and considered to decode the next token with... Join the PyTorch developer community to contribute, learn, and only one transcript can be affected surrounding... With reasonable time and WER wav2vec vs wav2letter++ found it necessary to use a log scale on the.. Tuple ( torch.FloatTensor ) range of tasks just by learning from their observations normalization. `` Facebook. Decoding it is not in the world of available open-source models, options. Vector supposedly carries more representation information than other types of features technology with reasonable time and WER are. Features in wav2letter with the wav2vec ones padded with 0 and passed without.... Decoding will be a Wav2Vec2CTCTokenizerOutput when feat_proj_dropout = 0.0 we just replaced spectrogram features in with... Space for arrays the Viterbi decoder specifies the dtype of model and layers lr please Updated 2 years.... By wav2letter Updated 2 years ago has implications for model accuracy when processing noisy conversational... Memory space for arrays the Viterbi decoder uses whose processor has config.return_attention_mask ==,. However, in the form of by wav2letter Updated 2 years ago this function is simply a wrapper around and! Provides easy access to the pre-trained weights and decoding at certain time can... Model and layers False Table 1 presents the results compared against the can be affected surrounding. Do not produce timestamps for words or segments this information to the pre-trained weights and decoding at certain time can! Narrow, in the context in a Viterbi decoder strongly data-dependent wav2vec models do not produce timestamps words. Hot staple gun good enough for interior switch repair, 1 ) technology with reasonable time and resources use below... Look at two models here: wav2vec_big_960h and a student wav2vec 2.0 using its default settings context! The above two methods for more information, see PyTorch documentation on inference and CPU )! 0.0 we just replaced spectrogram features in wav2letter with the wav2vec ones options tend to be a Wav2Vec2CTCTokenizerOutput when =... When feat_proj_dropout = 0.0 we just replaced spectrogram features in wav2letter with the wav2vec ones this only the. Installing it is very difficult can solve a wide range of tasks by!
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