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wav2vec2-xls-r-juznevesti-sr

Automatic Speech Recognition
Transformers
PyTorch
Serbian
wav2vec2
audio
Inference Endpoints
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  1. README.md +76 -0
  2. config.json +108 -0
  3. optimizer.pt +3 -0
  4. preprocessor_config.json +9 -0
  5. pytorch_model.bin +3 -0
  6. rng_state.pth +3 -0
  7. scaler.pt +3 -0
  8. scheduler.pt +3 -0
  9. trainer_state.json +252 -0
  10. training_args.bin +3 -0
  11. vocab.json +1 -0
README.md ADDED
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+ ---
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+ language: sr
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+ datasets:
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+ - juznevesti-sr
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+ tags:
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+ - audio
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+ - automatic-speech-recognition
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+ widget:
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+ - example_title: Croatian example 1
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+ src: https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/1800.m4a
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+ - example_title: Croatian example 2
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+ src: https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020578b.flac.wav
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+ - example_title: Croatian example 3
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+ src: https://huggingface.co/classla/wav2vec2-xls-r-parlaspeech-hr/raw/main/00020570a.flac.wav
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+ ---
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+
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+ # wav2vec2-large-juznevesti
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+
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+ This model for Serbian ASR is based on the [facebook/wav2vec2-large-slavic-voxpopuli-v2 model](https://huggingface.co/facebook/wav2vec2-large-slavic-voxpopuli-v2) and was fine-tuned with 58 hours of audio and transcripts from [Južne vesti](https://www.juznevesti.com/), programme '15 minuta'.
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+
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+
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+
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+ ## Metrics
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+
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+ Evaluation is performed on the dev and test portions of the JuzneVesti dataset
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+
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+ | | dev | test |
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+ |:----|---------:|---------:|
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+ | WER | 0.295206 | 0.290094 |
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+ | CER | 0.140766 | 0.137642 |
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+
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+
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+
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+ ## Usage in `transformers`
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+
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+ Tested with `transformers==4.18.0`, `torch==1.11.0`, and `SoundFile==0.10.3.post1`.
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+
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+ ```python
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+ from transformers import Wav2Vec2ProcessorWithLM, Wav2Vec2ForCTC
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+ import soundfile as sf
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+ import torch
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+ import os
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+ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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+ # load model and tokenizer
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+ processor = Wav2Vec2ProcessorWithLM.from_pretrained(
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+ "classla/wav2vec2-large-slavic-parlaspeech-hr-lm")
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+ model = Wav2Vec2ForCTC.from_pretrained("classla/wav2vec2-large-slavic-parlaspeech-hr-lm")
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+ # download the example wav files:
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+ os.system("wget https://huggingface.co/classla/wav2vec2-large-slavic-parlaspeech-hr-lm/raw/main/00020570a.flac.wav")
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+ # read the wav file
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+ speech, sample_rate = sf.read("00020570a.flac.wav")
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+ input_values = processor(speech, sampling_rate=sample_rate, return_tensors="pt").input_values.cuda()
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+ inputs = processor(speech, sampling_rate=sample_rate, return_tensors="pt")
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+ with torch.no_grad():
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+ logits = model(**inputs).logits
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+ transcription = processor.batch_decode(logits.numpy()).text[0]
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+
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+ # remove the raw wav file
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+ os.system("rm 00020570a.flac.wav")
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+
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+ transcription # 'velik broj poslovnih subjekata poslao je sa minusom velik dio'
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+ ```
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+
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+
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+
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+ ## Training hyperparameters
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+
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+ In fine-tuning, the following arguments were used:
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+
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+ | arg | value |
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+ |-------------------------------|-------|
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+ | `per_device_train_batch_size` | 16 |
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+ | `gradient_accumulation_steps` | 4 |
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+ | `num_train_epochs` | 8 |
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+ | `learning_rate` | 3e-4 |
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+ | `warmup_steps` | 500 |
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