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okuparinen/LIA_300m_simple
LIA_300m_simple is a automatic speech recognition model from okuparinen. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.
This is a fine-tuned model for automatic dialectal transcription of Norwegian dialect recordings. The model is based on the XLS-R large model. The model has been finetuned on old Norwegian dialect recordings and their…
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From the Hugging Face model README
This is a fine-tuned model for automatic dialectal transcription of Norwegian dialect recordings. The model is based on the XLS-R large model. The model has been finetuned on old Norwegian dialect recordings and their corresponding transcriptions. This model outputs simple transcription. The audio recordings are sampled at 16kHz.
You can use this model for automatic dialectal transcription of Norwegian dialects. Note that this model does not produce standard bokmål or nynorsk text.
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC, Wav2Vec2CTCTokenizer
from datasets import Dataset, Audio
import torch
import pandas as pd
ds = pd.read_csv('CSV_DATA.csv')
ds = ds.dropna(how='any', axis=0)
test = Dataset.from_pandas(skn_test)
test = test.cast_column("AUDIO_PATH_COLUMN", Audio(sampling_rate=16000))
tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("okuparinen/LIA_300m_simple", unk_token="[UNK]", pad_token="[PAD]", word_delimiter_token="|")
model = Wav2Vec2ForCTC.from_pretrained("okuparinen/LIA_300m_simple").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("okuparinen/LIA_300m_simple", tokenizer=tokenizer)
def prepare_dataset(batch):
audio = batch["AUDIO_PATH"]
batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
batch["input_length"] = len(batch["input_values"])
return batch
test_ready = test.map(prepare_dataset, remove_columns=test.column_names)
length = len(test)
predictions = []
for i in range(0, length, 1):
input_dict = processor(test_ready[i]["input_values"], return_tensors="pt", padding=True)
logits = model(input_dict.input_values.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)[0]
prediction = processor.decode(pred_ids)
predictions.append(prediction)
with open("OUTFILE.txt", "w") as f_pred:
for line in predictions:
f_pred.write(line + '\n')
The training data is an utterance-level version of the LIA Norwegian corpus. The utterance-level version is available at okuparinen/skn.
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