app.py

from transformers import pipeline, M2M100ForConditionalGeneration, M2M100Tokenizer
import gradio as gr
import re
import torch
from pyctcdecode import BeamSearchDecoderCTC

lmID = "aware-ai/german-lowercase-4gram-kenlm"
decoder = BeamSearchDecoderCTC.load_from_hf_hub(lmID)
p = pipeline("automatic-speech-recognition", model="aware-ai/robust-wav2vec2-base-german-lowercase", decoder=decoder)
ttp = pipeline("text2text-generation", model="aware-ai/marian-german-grammar")

model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_1.2B")
tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_1.2B")

def translate(src, tgt, text):
    src = src.split(" ")[-1][1:-1]
    tgt = tgt.split(" ")[-1][1:-1]

    # translate
    tokenizer.src_lang = src
    encoded_src = tokenizer(text, return_tensors="pt")
    generated_tokens = model.generate(**encoded_src, forced_bos_token_id=tokenizer.get_lang_id(tgt), use_cache=True)
    result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
    return result

def transcribe(audio):
    transcribed = p(audio, chunk_length_s=10, stride_length_s=(4, 2))["text"].lower()
    transcribed_corrected = ttp(re.sub("[^a-zA-Z0-9öäüÖÄÜ ]", " ",transcribed))[0]["generated_text"]
    
    return transcribed_corrected

def get_asr_interface():
    return gr.Interface(
        fn=transcribe, 
        inputs=[
            gr.inputs.Audio(source="microphone", type="filepath")
        ],
        outputs=[
            "textbox"
        ])
        

def get_translate_interface():
    langs = """Afrikaans (af), Amharic (am), Arabic (ar), Asturian (ast), Azerbaijani (az), Bashkir (ba), Belarusian (be), Bulgarian (bg), Bengali (bn), Breton (br), Bosnian (bs), Catalan; Valencian (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; Scottish Gaelic (gd), Galician (gl), Gujarati (gu), Hausa (ha), Hebrew (he), Hindi (hi), Croatian (hr), Haitian; Haitian Creole (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; Letzeburgesch (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; Flemish (nl), Norwegian (no), Northern Sotho (ns), Occitan (post 1500) (oc), Oriya (or), Panjabi; Punjabi (pa), Polish (pl), Pushto; Pashto (ps), Portuguese (pt), Romanian; Moldavian; Moldovan (ro), Russian (ru), Sindhi (sd), Sinhala; Sinhalese (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)"""
    lang_list = [lang.strip() for lang in langs.split(',')]
    return gr.Interface(translate, inputs=[gr.inputs.Dropdown(lang_list, label="Source Language"), gr.inputs.Dropdown(lang_list, label="Target Language"), 'text'], outputs=gr.outputs.Textbox(), title="Translate Between 100 languages")
        

interfaces = [
    get_asr_interface(),
    get_translate_interface(),
]

names = [
    "ASR",
    "translate",
]

gr.TabbedInterface(interfaces, names).launch(server_name = "0.0.0.0")