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import gradio as gr
from transformers import Wav2Vec2CTCTokenizer, Wav2Vec2ForCTC, Wav2Vec2ProcessorWithLM
import torch
import torchaudio
model_name = "Yehor/wav2vec2-xls-r-300m-uk-with-small-lm"
tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(model_name)
processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_name)
model = Wav2Vec2ForCTC.from_pretrained(model_name)
model.to("cpu")
# define function to read in sound file
def speech_file_to_array_fn(path, max_seconds=10):
batch = {"file": path}
speech_array, sampling_rate = torchaudio.load(batch["file"])
if sampling_rate != 16000:
transform = torchaudio.transforms.Resample(
orig_freq=sampling_rate, new_freq=16000
)
speech_array = transform(speech_array)
speech_array = speech_array[0]
if max_seconds > 0:
speech_array = speech_array[: max_seconds * 16000]
batch["speech"] = speech_array.numpy()
batch["sampling_rate"] = 16000
return batch
# tokenize
def inference(audio):
# read in sound file
# load dummy dataset and read soundfiles
sp = speech_file_to_array_fn(audio.name)
sample_rate = 16000
# stride_length_s is a tuple of the left and right stride length.
# With only 1 number, both sides get the same stride, by default
# the stride_length on one side is 1/6th of the chunk_length_s
input_values = processor(
sp["speech"],
sample_rate=sample_rate,
chunk_length_s=10,
stride_length_s=(4, 2),
return_tensors="pt",
).input_values
with torch.no_grad():
logits = model(input_values).logits
pred_ids = torch.argmax(logits, axis=-1).cpu().tolist()
prediction = tokenizer.decode(pred_ids[0], output_word_offsets=True)
time_offset = 320 / sample_rate
total_prediction = []
words = []
for item in prediction.word_offsets:
r = item
s = round(r['start_offset'] * time_offset, 2)
e = round(r['end_offset'] * time_offset, 2)
total_prediction.append(f"{s} - {e}: {r['word']}")
words.append(r['word'])
print(prediction[0])
return "\n".join(total_prediction) + "\n\n" + ' '.join(words)
inputs = gr.inputs.Audio(label="Input Audio", type="file")
outputs = gr.outputs.Textbox(label="Output Text")
title = model_name
description = f"Gradio demo for a {model_name}. To use it, simply upload your audio, or click one of the examples to load them. Read more at the links below. Currently supports .wav 16_000hz files"
article = "<p style='text-align: center'><a href='https://github.com/egorsmkv/wav2vec2-uk-demo' target='_blank'> Github repo</a> | <a href='<HF Space link>' target='_blank'>Pretrained model</a> | Made with help from <a href='https://github.com/robinhad' target='_blank'>@robinhad</a></p>"
examples = [
["long_1.wav"],
["mer_lviv_interview.wav"],
["short_1.wav"],
["tsn_2.wav"],
["tsn.wav"],
]
gr.Interface(
inference,
inputs,
outputs,
title=title,
description=description,
article=article,
examples=examples,
).launch()
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