readme: ---
  language: en
  datasets:
  - librispeech_asr
  tags:
  - audio
  - speech
  - automatic-speech-recognition
  - hf-asr-leaderboard
  license: apache-2.0
  widget:
  - example_title: Librispeech sample 1
    src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
  - example_title: Librispeech sample 2
    src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
  model-index:
  - name: sew-d-base-plus-400k-ft-ls100h
    results:
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: LibriSpeech (clean)
        type: librispeech_asr
        config: clean
        split: test
        args: 
          language: en
      metrics:
      - name: Test WER
        type: wer
        value: 4.34
    - task:
        name: Automatic Speech Recognition
        type: automatic-speech-recognition
      dataset:
        name: LibriSpeech (other)
        type: librispeech_asr
        config: other
        split: test
        args: 
          language: en
      metrics:
      - name: Test WER
        type: wer
        value: 9.45
  ---
  
  # SEW-D-base+
  
  [SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
  
  The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc...
  
  Paper: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870)
  
  Authors: Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi
  
  **Abstract**
  This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25-50% across different model sizes.
  
  The original model can be found under https://github.com/asappresearch/sew#model-checkpoints .
  
  # Usage
  To transcribe audio files the model can be used as a standalone acoustic model as follows:
  ```python
  from transformers import Wav2Vec2Processor, SEWDForCTC
  from datasets import load_dataset
  import soundfile as sf
  import torch
   
  # load the model and preprocessor
  processor = Wav2Vec2Processor.from_pretrained("asapp/sew-d-base-plus-400k-ft-ls100h")
  model = SEWDForCTC.from_pretrained("asapp/sew-d-base-plus-400k-ft-ls100h")
  
  # load the dummy dataset with speech samples
  ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
   
  # preprocess
  input_values = processor(ds[0]["audio"]["array"], return_tensors="pt").input_values  # Batch size 1
  
  # retrieve logits
  logits = model(input_values).logits
   
  # take argmax and decode
  predicted_ids = torch.argmax(logits, dim=-1)
  transcription = processor.batch_decode(predicted_ids)
   ```
  
   ## Evaluation
   
   This code snippet shows how to evaluate **asapp/sew-d-base-plus-400k-ft-ls100h** on LibriSpeech's "clean" and "other" test data.
   
  ```python
  from datasets import load_dataset
  from transformers import SEWDForCTC, Wav2Vec2Processor
  import torch
  from jiwer import wer
  
  librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
  
  model = SEWDForCTC.from_pretrained("asapp/sew-d-base-plus-400k-ft-ls100h").to("cuda")
  processor = Wav2Vec2Processor.from_pretrained("asapp/sew-d-base-plus-400k-ft-ls100h")
  
  def map_to_pred(batch):
      input_values = processor(batch["audio"][0]["array"], sampling_rate=16000, 
                               return_tensors="pt", padding="longest").input_values
      with torch.no_grad():
          logits = model(input_values.to("cuda")).logits
  
      predicted_ids = torch.argmax(logits, dim=-1)
      transcription = processor.batch_decode(predicted_ids)
      batch["transcription"] = transcription
      return batch
  
  result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["audio"])
  
  print("WER:", wer(result["text"], result["transcription"]))
  ```
  
  *Result (WER)*:
  
  | "clean" | "other" |
  | --- | --- |
  | 4.34 | 9.45 |
  