Instructions to use sanchit-gandhi/flax-wav2vec2-2-bart-large-cv8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sanchit-gandhi/flax-wav2vec2-2-bart-large-cv8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sanchit-gandhi/flax-wav2vec2-2-bart-large-cv8")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("sanchit-gandhi/flax-wav2vec2-2-bart-large-cv8") model = AutoModelForMultimodalLM.from_pretrained("sanchit-gandhi/flax-wav2vec2-2-bart-large-cv8") - Notebooks
- Google Colab
- Kaggle
File size: 1,071 Bytes
aca4d02 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | command:
- python3
- ${program}
- --predict_with_generate
- --do_lower_case
- --do_train
- --do_eval
- --do_predict
- ${args}
method: random
metric:
goal: minimize
name: eval/wer
parameters:
dataset_cache_dir:
value: /home/sanchitgandhi/cache/huggingface/datasets
dataset_config_name:
value: all
dataset_name:
value: mozilla-foundation/common_voice_8_0
eval_split_name:
value: validation
generation_max_length:
value: 200
generation_num_beams:
value: 5
length_column_name:
value: input_length
length_penalty:
distribution: log_uniform
max: 0.69
min: -2.3
logging_steps:
value: 25
max_duration_in_seconds:
value: 20
max_target_length:
value: 128
model_name_or_path:
value: ./
output_dir:
value: ./
per_device_eval_batch_size:
value: 8
preprocessing_num_workers:
value: 16
test_split_name:
value: test
text_column_name:
value: text
warmup_steps:
value: 500
program: run_flax_generation_seq2seq.py
project: flax-wav2vec2-2-bart-large-cv8
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