đ Wav2Vec2-Large-LV60-TIMIT
This model is fine-tuned from facebook/wav2vec2-large-lv60 on the timit_asr dataset. It's designed for automatic speech recognition, and when using it, ensure your speech input is sampled at 16kHz.
đ Quick Start
The model can be used directly (without a language model) as follows:
đģ Usage Examples
Basic Usage
import soundfile as sf
import torch
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
model_name = "hktayal345/wav2vec2-large-lv60-timit-asr"
processor = Wav2Vec2Processor.from_pretrained(model_name)
model = Wav2Vec2ForCTC.from_pretrained(model_name)
model.eval()
dataset = load_dataset("timit_asr", split="test").shuffle().select(range(10))
char_translations = str.maketrans({"-": " ", ",": "", ".": "", "?": ""})
def prepare_example(example):
example["speech"], _ = sf.read(example["file"])
example["text"] = example["text"].translate(char_translations)
example["text"] = " ".join(example["text"].split())
example["text"] = example["text"].lower()
return example
dataset = dataset.map(prepare_example, remove_columns=["file"])
inputs = processor(dataset["speech"], sampling_rate=16000, return_tensors="pt", padding="longest")
with torch.no_grad():
predicted_ids = torch.argmax(model(inputs.input_values).logits, dim=-1)
predicted_ids[predicted_ids == -100] = processor.tokenizer.pad_token_id
predicted_transcripts = processor.tokenizer.batch_decode(predicted_ids)
for reference, predicted in zip(dataset["text"], predicted_transcripts):
print("reference:", reference)
print("predicted:", predicted)
print("--")
Here's the output:
reference: the emblem depicts the acropolis all aglow
predicted: the amblum depicts the acropolis all a glo
--
reference: don't ask me to carry an oily rag like that
predicted: don't ask me to carry an oily rag like that
--
reference: they enjoy it when i audition
predicted: they enjoy it when i addition
--
reference: set aside to dry with lid on sugar bowl
predicted: set aside to dry with a litt on shoogerbowl
--
reference: a boring novel is a superb sleeping pill
predicted: a bor and novel is a suberb sleeping peel
--
reference: only the most accomplished artists obtain popularity
predicted: only the most accomplished artists obtain popularity
--
reference: he has never himself done anything for which to be hated which of us has
predicted: he has never himself done anything for which to be hated which of us has
--
reference: the fish began to leap frantically on the surface of the small lake
predicted: the fish began to leap frantically on the surface of the small lake
--
reference: or certain words or rituals that child and adult go through may do the trick
predicted: or certain words or rituals that child an adult go through may do the trick
--
reference: are your grades higher or lower than nancy's
predicted: are your grades higher or lower than nancies
--
đ Documentation
Fine-Tuning Script
You can find the script used to produce this model here.
Note: This model can be fine-tuned further; trainer_state.json shows useful details, namely the last state (this checkpoint):
{
"epoch": 29.51,
"eval_loss": 25.424150466918945,
"eval_runtime": 182.9499,
"eval_samples_per_second": 9.183,
"eval_wer": 0.1351704233095107,
"step": 8500
}
đ License
This model is licensed under the Apache-2.0 license.
đ Additional Information
Property |
Details |
Model Type |
Fine-tuned Wav2Vec2 |
Training Data |
timit_asr |
Tags |
audio, automatic-speech-recognition, speech |