app.py

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from transformers.pipelines.audio_utils import ffmpeg_read
import gradio as gr
#from transformers import WhisperForConditionalGeneration, WhisperProcessor
#from transformers.models.whisper.tokenization_whisper import LANGUAGES
#from transformers.pipelines.audio_utils import ffmpeg_read

model_id = "openai/whisper-large-v2"
device = "cuda" if torch.cuda.is_available() else "cpu"


LANGUANGE_MAP = {
    0: 'Arabic',
    1: 'Basque',
    2: 'Breton',
    3: 'Catalan',
    4: 'Chinese_China',
    5: 'Chinese_Hongkong',
    6: 'Chinese_Taiwan',
    7: 'Chuvash',
    8: 'Czech',
    9: 'Dhivehi',
    10: 'Dutch',
    11: 'English',
    12: 'Esperanto',
    13: 'Estonian',
    14: 'French',
    15: 'Frisian',
    16: 'Georgian',
    17: 'German',
    18: 'Greek',
    19: 'Hakha_Chin',
    20: 'Indonesian',
    21: 'Interlingua',
    22: 'Italian',
    23: 'Japanese',
    24: 'Kabyle',
    25: 'Kinyarwanda',
    26: 'Kyrgyz',
    27: 'Latvian',
    28: 'Maltese',
    29: 'Mongolian',
    30: 'Persian',
    31: 'Polish',
    32: 'Portuguese',
    33: 'Romanian',
    34: 'Romansh_Sursilvan',
    35: 'Russian',
    36: 'Sakha',
    37: 'Slovenian',
    38: 'Spanish',
    39: 'Swedish',
    40: 'Tamil',
    41: 'Tatar',
    42: 'Turkish',
    43: 'Ukranian',
    44: 'Welsh'
 }


import whisper

# define function for transcription
def transcribe(Microphone, File_Upload, URL):
    warn_output = ""
    if (Microphone is not None) and (File_Upload is not None):
        warn_output = "WARNING: You've uploaded an audio file and used the microphone. " \
                      "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
        file = Microphone

    elif (Microphone is None) and (File_Upload is None):
        return "ERROR: You have to either use the microphone or upload an audio file"

    elif Microphone is not None:
        file = Microphone
        
    else if URL:
        link = YouTube(url)
        source = link.streams.filter(only_audio=True)[0].download(filename="audio.mp4")
    else:
        file = File_Upload
    

    language = None

    options = whisper.DecodingOptions(without_timestamps=True)

    loaded_model = whisper.load_model("base")
    transcript = loaded_model.transcribe(file, language=language)

    return detect_language(transcript["text"])

def detect_language(sentence):
    
    model_ckpt = "barto17/language-detection-fine-tuned-on-xlm-roberta-base"
    model = AutoModelForSequenceClassification.from_pretrained(model_ckpt)
    tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
    tokenized_sentence = tokenizer(sentence, return_tensors='pt')
    output = model(**tokenized_sentence)
    predictions = torch.nn.functional.softmax(output.logits, dim=-1)
    probability, pred_idx = torch.max(predictions, dim=-1)
    language = LANGUANGE_MAP[pred_idx.item()]
    return sentence, language, probability.item()


"""
processor = WhisperProcessor.from_pretrained(model_id)
model = WhisperForConditionalGeneration.from_pretrained(model_id)
model.eval()
model.to(device)


bos_token_id = processor.tokenizer.all_special_ids[-106]
decoder_input_ids = torch.tensor([bos_token_id]).to(device)


def process_audio_file(file, sampling_rate):
    with open(file, "rb") as f:
        inputs = f.read()

    audio = ffmpeg_read(inputs, sampling_rate)
    print(audio)
    return audio

def transcribe(Microphone, File_Upload):
    
    warn_output = ""
    if (Microphone is not None) and (File_Upload is not None):
        warn_output = "WARNING: You've uploaded an audio file and used the microphone. " \
                      "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
        file = Microphone

    elif (Microphone is None) and (File_Upload is None):
        return "ERROR: You have to either use the microphone or upload an audio file"

    elif Microphone is not None:
        file = Microphone
    else:
        file = File_Upload

    sampling_rate = processor.feature_extractor.sampling_rate


    audio_data = process_audio_file(file, sampling_rate)

    input_features = processor(audio_data, return_tensors="pt").input_features
    
    with torch.no_grad():
        logits = model.forward(input_features.to(device), decoder_input_ids=decoder_input_ids).logits
    
    pred_ids = torch.argmax(logits, dim=-1)
    transcription = processor.decode(pred_ids[0])
    
    language, probability = detect_language(transcription)
    
    return transcription.capitalize(), language, probability
"""

examples=['sample1.mp3', 'sample2.mp3', 'sample3.mp3']
examples = [[f"./{f}"] for f in examples]

outputs=gr.outputs.Label(label="Language detected:")
article = """
Fine-tuned on xlm-roberta-base model.\n
Supported languages:\n 
    'Arabic', 'Basque', 'Breton', 'Catalan', 'Chinese_China', 'Chinese_Hongkong', 'Chinese_Taiwan', 'Chuvash', 'Czech', 
    'Dhivehi', 'Dutch', 'English', 'Esperanto', 'Estonian', 'French', 'Frisian', 'Georgian', 'German', 'Greek', 'Hakha_Chin', 
    'Indonesian', 'Interlingua', 'Italian', 'Japanese', 'Kabyle', 'Kinyarwanda', 'Kyrgyz', 'Latvian', 'Maltese', 
    'Mangolian', 'Persian', 'Polish', 'Portuguese', 'Romanian', 'Romansh_Sursilvan', 'Russian', 'Sakha', 'Slovenian', 
    'Spanish', 'Swedish', 'Tamil', 'Tatar', 'Turkish', 'Ukranian', 'Welsh'
"""

gr.Interface(
    fn=transcribe,
    inputs=[
        gr.inputs.Audio(source="microphone", type='filepath', optional=True),
        gr.inputs.Audio(source="upload", type='filepath', optional=True),
        gr.Textbox(label="Paste YouTube link here"),
    ],
    
    outputs=[
        gr.outputs.Textbox(label="Transcription"),
        gr.outputs.Textbox(label="Language"),
        gr.Number(label="Probability"),
    ],
    
    verbose=True,
    examples = examples,
    title="Language Identification from Audio",
    description="Detect the Language from Audio.",
    article=article,
    theme="huggingface"
).launch()