Instructions to use esc-bench/wav2vec2-aed-chime4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-bench/wav2vec2-aed-chime4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-bench/wav2vec2-aed-chime4")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("esc-bench/wav2vec2-aed-chime4") model = AutoModelForSpeechSeq2Seq.from_pretrained("esc-bench/wav2vec2-aed-chime4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Sanchit Gandhi commited on
Commit ·
9a404bc
1
Parent(s): 1686843
Correct scripts
Browse files- README.md +2 -2
- run_chime4.sh +33 -0
README.md
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```python
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#!/usr/bin/env bash
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python run_flax_speech_recognition_seq2seq.py \
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--dataset_name="esc/esc-datasets" \
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--model_name_or_path="esc/wav2vec2-aed-pretrained" \
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--dataset_config_name="chime4" \
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--output_dir="./" \
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--wandb_name="wav2vec2-aed-chime4" \
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```python
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#!/usr/bin/env bash
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python run_flax_speech_recognition_seq2seq.py \
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--dataset_name="esc-benchmark/esc-datasets" \
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--model_name_or_path="esc-benchmark/wav2vec2-aed-pretrained" \
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--dataset_config_name="chime4" \
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--output_dir="./" \
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--wandb_name="wav2vec2-aed-chime4" \
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run_chime4.sh
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#!/usr/bin/env bash
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python run_flax_speech_recognition_seq2seq.py \
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--dataset_name="esc-benchmark/esc-datasets" \
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--model_name_or_path="esc-benchmark/wav2vec2-aed-pretrained" \
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--dataset_config_name="chime4" \
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--output_dir="./" \
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--wandb_name="wav2vec2-aed-chime4" \
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--wandb_project="wav2vec2-aed" \
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--per_device_train_batch_size="8" \
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--per_device_eval_batch_size="4" \
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--logging_steps="25" \
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--max_steps="50000" \
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--eval_steps="10000" \
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--save_steps="10000" \
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--generation_max_length="40" \
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--generation_num_beams="1" \
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--generation_length_penalty="1.2" \
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--final_generation_max_length="200" \
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--final_generation_num_beams="5" \
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--learning_rate="1e-4" \
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--warmup_steps="500" \
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--hidden_dropout="0.2" \
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--activation_dropout="0.2" \
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--feat_proj_dropout="0.2" \
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--overwrite_output_dir \
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--gradient_checkpointing \
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--freeze_feature_encoder \
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--predict_with_generate \
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--do_eval \
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--do_train \
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--do_predict \
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--push_to_hub \
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--use_auth_token
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