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import gradio as gr
import librosa
from transformers import AutoFeatureExtractor, AutoTokenizer, SpeechEncoderDecoderModel
model_name = "facebook/wav2vec2-xls-r-2b-22-to-16"
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name, use_auth_token="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")
tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token="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", use_fast=False)
model = SpeechEncoderDecoderModel.from_pretrained(model_name, use_auth_token="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")
def process_audio_file(file):
data, sr = librosa.load(file)
if sr != 16000:
data = librosa.resample(data, sr, 16000)
print(data.shape)
input_values = feature_extractor(data, return_tensors="pt").input_values
return input_values
def transcribe(file, target_language):
target_code = target_language.split("(")[-1].split(")")[0]
forced_bos_token_id = MAPPING[target_code]
input_values = process_audio_file(file)
sequences = model.generate(input_values, forced_bos_token_id=forced_bos_token_id)
transcription = tokenizer.batch_decode(sequences, skip_special_tokens=True)
return transcription[0]
target_language = [
"English (en)",
"German (de)",
"Turkish (tr)",
"Persian (fa)",
"Swedish (sv)",
"Mongolian (mn)",
"Chinese (zh)",
"Welsh (cy)",
"Catalan (ca)",
"Slovenian (sl)",
"Estonian (et)",
"Indonesian (id)",
"Arabic (ar)",
"Tamil (ta)",
"Latvian (lv)",
"Japanese (ja)",
]
MAPPING = {
"en": 250004,
"de": 250003,
"tr": 250023,
"fa": 250029,
"sv": 250042,
"mn": 250037,
"zh": 250025,
"cy": 250007,
"ca": 250005,
"sl": 250052,
"et": 250006,
"id": 250032,
"ar": 250001,
"ta": 250044,
"lv": 250017,
"ja": 250012,
}
iface = gr.Interface(
fn=transcribe,
inputs=[
gr.inputs.Audio(source="microphone", type='filepath'),
gr.inputs.Dropdown(target_language),
],
outputs="text",
layout="horizontal",
theme="huggingface",
title="XLS-R 300M 22-to-16 Speech Translation",
description="A simple interface to translate from 22 input spoken languages to 16 written languages.",
)
iface.launch()
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