ASPMIR-YorTTS.py

#!/usr/bin/env python
# coding: utf-8

# In[1]:


#Install All the Required Dependencies
#!pip3 install torch torchvision torchaudio
#!pip install transformers ipywidgets gradio --upgrade
#!pip install --upgrade transformers accelerate
#!pip install --upgrade gradio
#!pip install nltk
#!pip install jiwer
#!pip install sentencepiece
#!pip install sacremoses
#!pip install soundfile
#!pip install librosa numpy jiwer nltk
#!pip install --upgrade pip 
#!pip install huggingface_hub


# In[2]:


#Import Required Libraries
from transformers import pipeline
from jiwer import wer
from transformers import VitsModel, AutoTokenizer, set_seed
import torch
import soundfile as sf
import librosa
from scipy.spatial.distance import euclidean
import numpy as np
import string
import os
from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
from nltk.translate.meteor_score import meteor_score
import string
import numpy as np
import librosa
from scipy.spatial.distance import euclidean
import string


# In[3]:


import nltk
nltk.download('wordnet')
nltk.download('omw-1.4')  # Optional if using WordNet's multilingual features
import nltk
print(nltk.data.path)
import nltk
nltk.data.path.append('./nltk_data')


# In[4]:


#Define all Utility Functions
# Function to compute BLEU score
def compute_bleu(reference_text, predicted_text):
    """
    Computes the BLEU score for a single translation.
    :param reference_text: The ground truth text (in Yoruba).
    :param predicted_text: The machine-generated translation text (in Yoruba).
    :return: BLEU score (float).
    """
    print("The Reference Text = ", reference_text)
    print("The Predicted Text = ",predicted_text)
    # Tokenize the reference and predicted texts
    reference_tokens = [reference_text.split()]  # Reference should be wrapped in a list
    predicted_tokens = predicted_text.split()

    # Add smoothing to handle cases with few n-gram matches
    smoothing_function = SmoothingFunction().method1

    # Compute BLEU score
    bleu_score = sentence_bleu(reference_tokens, predicted_tokens, smoothing_function=smoothing_function)
    #print("The Computed bleu_score in the Compute_Blue Fn = ",bleu_score)
    return round(bleu_score,2)
# Function to compute Word Error Rate (WER)
def compute_wer(reference_text, predicted_text):
    """
    Computes the Word Error Rate (WER) for a single translation.
    :param reference_text: The ground truth text (in Yoruba).
    :param predicted_text: The machine-generated translation text (in Yoruba).
    :return: WER score (float).
    """
    # Normalize text: lowercase and remove punctuation
    reference_text = reference_text.lower().translate(str.maketrans('', '', string.punctuation))
    predicted_text = predicted_text.lower().translate(str.maketrans('', '', string.punctuation))

    # Compute WER
    wer_score = wer(reference_text, predicted_text)

    return round(wer_score,2)

# Function to compute METEOR score
def compute_meteor(reference_text, predicted_text):
    """
    Computes the METEOR score for a single translation.
    :param reference_text: The ground truth text (in Yoruba).
    :param predicted_text: The machine-generated translation text (in Yoruba).
    :return: METEOR score (float).
    """
    # Normalize text: lowercase and remove punctuation
    reference_text = reference_text.lower().translate(str.maketrans('', '', string.punctuation))
    predicted_text = predicted_text.lower().translate(str.maketrans('', '', string.punctuation))

    # Tokenize text into lists of words
    reference_tokens = reference_text.split()
    predicted_tokens = predicted_text.split()

    # Compute METEOR score
    meteor = meteor_score([reference_tokens], predicted_tokens)
    
    return round(meteor,2)

# Function to compute Mel Cepstral Distance (MCD)
def compute_mcd(ground_truth_audio_path, predicted_audio_path):
    """
    Computes the Mel Cepstral Distance (MCD) between two audio files.
    :param ground_truth_audio_path: Path to the ground truth audio file.
    :param predicted_audio_path: Path to the predicted audio file.
    :return: MCD score (float).
    """
    # Load audio files
    y_true, sr_true = librosa.load(ground_truth_audio_path, sr=16000)
    y_pred, sr_pred = librosa.load(predicted_audio_path, sr=16000)

    # Ensure the sampling rates match
    assert sr_true == sr_pred, "Sampling rates do not match between audio files."

    # Compute MFCCs
    mfcc_true = librosa.feature.mfcc(y=y_true, sr=sr_true, n_mfcc=13).T
    mfcc_pred = librosa.feature.mfcc(y=y_pred, sr=sr_pred, n_mfcc=13).T

    # Align the MFCC frames
    min_frames = min(len(mfcc_true), len(mfcc_pred))
    mfcc_true = mfcc_true[:min_frames]
    mfcc_pred = mfcc_pred[:min_frames]

    # Compute the Euclidean distance for each frame and average
    mcd = 0.0
    for i in range(min_frames):
        mcd += euclidean(mfcc_true[i], mfcc_pred[i])
    mcd = (10.0 / np.log(10)) * (mcd / min_frames)

    return round(mcd,2)


# In[5]:


#Define Translation and Synthesis Function
def translate_transformers(modelName, sourceLangText):
    #results = translation_pipeline(input_text)
    translation_pipeline = pipeline('translation_en_to_yo', model = modelName, max_length=500)
    translated_text = translation_pipeline(sourceLangText) #translator(text)[0]["translation_text"]
    translated_text_target = translated_text[0]['translation_text']
    #reference_translations = "awon apositeli, awon woli, awon ajinrere ati awon oluso agutan ati awon oluko." #'recorder_2024-01-13_11-24-41_453538.wav'#"My name is Joy, I love reading"
   
    #TTS for the translated_text_target
    #TTS Exp1
    ttsModel = VitsModel.from_pretrained("facebook/mms-tts-yor")
    tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-yor")
    ttsInputs = tokenizer(translated_text_target, return_tensors="pt")
    set_seed(555)  # make deterministic
    with torch.no_grad():
        ttsOutput = ttsModel(**ttsInputs).waveform
    #Convert the tensor to a numpy array
    ttsWaveform = ttsOutput.numpy()[0]    
    #Save the waveform to an audio file
    #sf.write('output.wav', waveform, 22050)
    sf.write('ttsOutput.wav', ttsWaveform, 16000)
    
    # Sample ground truth and predicted text2text translations for Clinical Text
    #ground_truth_text = "Àrùn jẹjẹrẹ ọmú jẹ́ ọ̀kan pàtàkì lára ohun tó ń ṣàkóbá fún ìlera gbogbo ènìyàn ní Nàìjíríà, ó sì jẹ́ ọ̀kan pàtàkì lára ohun tó ń fa ikú àwọn obìnrin tí àrùn jẹjẹrẹ ń pa lórílẹ̀-èdè náà."
    #predicted_text = translated_text_target #"<extra_id_0> breast cancer is a"

    # Sample ground truth and predicted text2text translations for News Text
    #ground_truth_text = "Wọ́n ní ìgbà àkọ́kọ́ nìyí tí irú ìwà ipá bẹ́ẹ̀ máa wáyé ní ìpínlẹ̀ Ondo."
    #predicted_text = translated_text_target #"<extra_id_0> breast cancer is a"

    # Sample ground truth and predicted text2text translations for Religion Text
    ground_truth_text = "Àwọn aposteli, àwọn wòlíì, àwọn ajíhìnrere, àwọn olùṣọ́-àgùntàn àti àwọn olùkọ́."
    predicted_text = translated_text_target #"<extra_id_0> breast cancer is a"
    
    #Compute bleu_score
    bleu_score = compute_bleu(ground_truth_text, predicted_text)
    print(f"Bleu Score (BLEU): {bleu_score:.2f}")
    
    #Compute WER
    wer_score = compute_wer(ground_truth_text, predicted_text)
    print(f"Word Error Rate (WER): {wer_score:.2f}")

    #Compute METEOR
    meteor = compute_meteor(ground_truth_text, predicted_text)
    print(f"METEOR Score: {meteor:.2f}")

    # Paths to sample audio files for MCD computation in current directory
    ground_truth_audio = os.path.join(os.getcwd(), "gt_ttsOutput.wav")
    predicted_audio = os.path.join(os.getcwd(), "ttsOutput.wav")

    # Compute Mel Cepstral Distance (MCD)
    try:
        mcd = compute_mcd(ground_truth_audio, predicted_audio)
        print(f"Mel Cepstral Distance (MCD): {mcd:.2f}")
    except Exception as e:
        print(f"Error computing MCD: {e}")
    
    return translated_text_target,bleu_score,wer_score,meteor,mcd,'ttsOutput.wav'


# In[6]:


#Define User Interface Function using Gradio and IPython Libraries
import gradio as gr
from IPython.display import Audio
interface = gr.Interface(
    fn=translate_transformers,
    inputs=[
        gr.Dropdown(["Davlan/byt5-base-eng-yor-mt", #Exp1
                     "Davlan/m2m100_418M-eng-yor-mt", #Exp2
                     "Davlan/mbart50-large-eng-yor-mt", #Exp3
                     "Davlan/mt5_base_eng_yor_mt", #Exp4
                     "omoekan/opus-tatoeba-eng-yor", #Exp5
                     "masakhane/afrimt5_en_yor_news", #Exp6
                     "masakhane/afrimbart_en_yor_news", #Exp7
                     "masakhane/afribyt5_en_yor_news", #Exp8
                     "masakhane/byt5_en_yor_news", #Exp9
                     "masakhane/mt5_en_yor_news", #Exp10
                     "masakhane/mbart50_en_yor_news", #Exp11
                     "masakhane/m2m100_418M_en_yor_news", #Exp12
                     "masakhane/m2m100_418M_en_yor_rel_news", #Exp13
                     "masakhane/m2m100_418M_en_yor_rel_news_ft", #Exp14
                     "masakhane/m2m100_418M_en_yor_rel", #Exp15
                     "dabagyan/menyo_en2yo", #Exp16
                     #"facebook/nllb-200-distilled-600M", #Exp17
                     #"facebook/nllb-200-3.3B", #Exp18
                     #"facebook/nllb-200-1.3B", #Exp19
                     #"facebook/nllb-200-distilled-1.3B",  #Exp20
                     #"keithhon/nllb-200-3.3B" #Exp21
                     #"CohereForAI/aya-101" #Exp22
                     "facebook/m2m100_418M", #Exp17
                     #"facebook/m2m100_1.2B",#Exp18
                     #"facebook/m2m100-12B-avg-5-ckpt", #Exp19
                     "google/mt5-base", #Exp20
                     "google/byt5-large" #Exp21
                     ], 
                     label="Select Finetuned Eng2Yor Translation Model"),
        gr.Textbox(lines=2, placeholder="Enter English Text Here...", label="English Text")  
    ],
    #outputs = "text",
    #outputs=outputs=["text", "text"],#"text"
    #outputs= gr.Textbox(value="text", label="Translated Text"),
    outputs=[
        gr.Textbox(value="text", label="Translated Yoruba Text"),
        #gr.Textbox(value="text", label=translated_text_actual),
        gr.Textbox(value="number", label="BLEU SCORE"),
        gr.Textbox(value="number", label="WER(WORD ERROR RATE) SCORE - The Lower the Better"),
        gr.Textbox(value="number", label="METEOR SCORE"),
        gr.Textbox(value="number", label="MCD(MEL CESPRAL DISTANCE) SCORE"),
        gr.Audio(type="filepath", label="Click to Generate Yoruba Speech from the Translated Text")
    ],
    title="ASPMIR-MACHINE-TRANSLATION-TESTBED FOR LOW RESOURCED AFRICAN LANGUAGES",
    #gr.Markdown("**This Tool Allows Developers and Researchers to Carry Out Experiments on Low Resourced African Languages with State-of-the-Art NMT Finetuned Models.**"),
    description="{This Tool Allows Developers and Researchers to Carry Out Experiments on Low Resourced African Languages with State-of-the-Art Pretrained or Finetuned Models.}"
)
#interface.launch(share=True)


# In[7]:


if __name__ == "__main__":
    interface.launch(share=True)