helper.py

import torch 
import torchaudio
import torch
from transformers import pipeline
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import librosa
from huggingface_hub.hf_api import HfFolder
from transformers import MarianMTModel, MarianTokenizer
from elevenlabs import set_api_key
from elevenlabs import clone, generate, play
HfFolder.save_token('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')
access_token = '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'


tokenizer_en_hn = AutoTokenizer.from_pretrained("vasudevgupta/mbart-bhasha-hin-eng")
model_translate_en_hm = AutoModelForSeq2SeqLM.from_pretrained("vasudevgupta/mbart-bhasha-hin-eng")

processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")
model_speech = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200")



def parse_transcription(wav_file):
    # load audio
    audio_input, sample_rate = librosa.load(wav_file, sr=16000)

    # pad input values and return pt tensor
    input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values

    # INFERENCE
    # retrieve logits & take argmax
    logits = model_speech(input_values).logits
    predicted_ids = torch.argmax(logits, dim=-1)

    # transcribe
    transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
    return transcription

def hindi_to_english(text): 
  inputs = tokenizer_en_hn.encode(
    text, return_tensors="pt",padding=True,max_length=512,truncation=True)

  outputs = model_translate_en_hm.generate(
    inputs, max_length=128, num_beams=4, early_stopping=True
    )
  translated = tokenizer_en_hn.decode(outputs[0]).replace('<pad>',"").replace('<s>', "").strip().lower()
  return translated

def translate_english_to_hindi(input_text):
    # Load the pre-trained English to Hindi translation model and tokenizer
    model_name = "Helsinki-NLP/opus-mt-en-hi"
    tokenizer = MarianTokenizer.from_pretrained(model_name)
    model = MarianMTModel.from_pretrained(model_name)

    # Tokenize the input text and generate translation
    inputs = tokenizer(input_text, return_tensors="pt", padding=True)
    translated_ids = model.generate(inputs.input_ids)

    # Decode the translated output
    translated_text = tokenizer.decode(translated_ids[0], skip_special_tokens=True)

    return translated_text

def hindi_tts(text): 
   audio = generate(
  text=text,
  voice="Customer Service",
  model="eleven_monolingual_v1"
    )
   
   return audio