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import gradio as gr
import json
import os
import string
import re
import torch
from transformers import pipeline
from transformers import M2M100Tokenizer, M2M100ForConditionalGeneration
import fasttext
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(repo_id="cis-lmu/glotlid", filename="model.bin")
identification_model = fasttext.load_model(model_path)
def lang_ident(text):
label, array = identification_model.predict(text)
label = get_name(label[0].split('__')[-1].replace('_Hans', '_Hani').replace('_Hant', '_Hani'))
return {language : label[0], socore : array[0]}
pretrained_model: str = "facebook/m2m100_1.2B"
cache_dir: str = "models/"
tokenizer = M2M100Tokenizer.from_pretrained(pretrained_model, cache_dir=cache_dir)
translation_model = M2M100ForConditionalGeneration.from_pretrained(
pretrained_model, cache_dir=cache_dir)
transcription = pipeline("automatic-speech-recognition", model= "openai/whisper-base")
clasification = pipeline(
"audio-classification",
model="anton-l/xtreme_s_xlsr_300m_minds14",
)
def language_names(json_path):
with open(json_path, 'r') as json_file:
data = json.load(json_file)
return data
label2name = language_names("assetslanguage_names.json")
def get_name(label):
"""Get the name of language from label"""
iso_3 = label.split('_')[0]
name = label2name[iso_3]
return name
def audio_a_text(audio):
text = transcription(audio)["text"]
return text
def text_to_sentimient(audio):
#text = transcription(audio)["text"]
return clasification(audio)
lang_id = {
"Afrikaans": "af",
"Amharic": "am",
"Arabic": "ar",
"Asturian": "ast",
"Azerbaijani": "az",
"Bashkir": "ba",
"Belarusian": "be",
"Bulgarian": "bg",
"Bengali": "bn",
"Breton": "br",
"Bosnian": "bs",
"Catalan": "ca",
"Cebuano": "ceb",
"Czech": "cs",
"Welsh": "cy",
"Danish": "da",
"German": "de",
"Greeek": "el",
"English": "en",
"Spanish": "es",
"Estonian": "et",
"Persian": "fa",
"Fulah": "ff",
"Finnish": "fi",
"French": "fr",
"Western Frisian": "fy",
"Irish": "ga",
"Gaelic": "gd",
"Galician": "gl",
"Gujarati": "gu",
"Hausa": "ha",
"Hebrew": "he",
"Hindi": "hi",
"Croatian": "hr",
"Haitian": "ht",
"Hungarian": "hu",
"Armenian": "hy",
"Indonesian": "id",
"Igbo": "ig",
"Iloko": "ilo",
"Icelandic": "is",
"Italian": "it",
"Japanese": "ja",
"Javanese": "jv",
"Georgian": "ka",
"Kazakh": "kk",
"Central Khmer": "km",
"Kannada": "kn",
"Korean": "ko",
"Luxembourgish": "lb",
"Ganda": "lg",
"Lingala": "ln",
"Lao": "lo",
"Lithuanian": "lt",
"Latvian": "lv",
"Malagasy": "mg",
"Macedonian": "mk",
"Malayalam": "ml",
"Mongolian": "mn",
"Marathi": "mr",
"Malay": "ms",
"Burmese": "my",
"Nepali": "ne",
"Dutch": "nl",
"Norwegian": "no",
"Northern Sotho": "ns",
"Occitan": "oc",
"Oriya": "or",
"Panjabi": "pa",
"Polish": "pl",
"Pushto": "ps",
"Portuguese": "pt",
"Romanian": "ro",
"Russian": "ru",
"Sindhi": "sd",
"Sinhala": "si",
"Slovak": "sk",
"Slovenian": "sl",
"Somali": "so",
"Albanian": "sq",
"Serbian": "sr",
"Swati": "ss",
"Sundanese": "su",
"Swedish": "sv",
"Swahili": "sw",
"Tamil": "ta",
"Thai": "th",
"Tagalog": "tl",
"Tswana": "tn",
"Turkish": "tr",
"Ukrainian": "uk",
"Urdu": "ur",
"Uzbek": "uz",
"Vietnamese": "vi",
"Wolof": "wo",
"Xhosa": "xh",
"Yiddish": "yi",
"Yoruba": "yo",
"Chinese": "zh",
"Zulu": "zu",
}
def translation_text(source_lang, target_lang, user_input):
src_lang = lang_id[source_lang]
trg_lang = lang_id[target_lang]
tokenizer.src_lang = src_lang
with torch.no_grad():
encoded_input = tokenizer(user_input, return_tensors="pt")
generated_tokens = translation_model.generate(**encoded_input, forced_bos_token_id=tokenizer.get_lang_id(trg_lang))
translated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
return translated_text
def print_s(source_lang, target_lang, text0):
print(source_lang)
return lang_id[source_lang], lang_id[target_lang], text0
demo = gr.Blocks()
with demo:
gr.Markdown("Speech analyzer")
audio = gr.Audio(type="filepath", label = "Upload a file")
text0 = gr.Textbox()
text = gr.Textbox()
source_lang = gr.Dropdown(label="Source lang", choices=list(lang_id.keys()), value=list(lang_id.keys())[0])
target_lang = gr.Dropdown(label="target lang", choices=list(lang_id.keys()), value=list(lang_id.keys())[0])
#gr.Examples(examples = list(lang_id.keys()),
# inputs=[
# source_lang])
b1 = gr.Button("convert to text")
b3 = gr.Button("translate")
b3.click(translation_text, inputs = [source_lang, target_lang, text0], outputs = text)
b1.click(audio_a_text, inputs=audio, outputs=text)
b2 = gr.Button("Classification of language")
b2.click(lang_ident,inputs = text0, outputs=text)
demo.launch()
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