add secrets

app.py

@@ -3,7 +3,7 @@ import librosa
 import soundfile as sf
 import torch
 import warnings
-
+import os 
 from transformers import Wav2Vec2ProcessorWithLM, Wav2Vec2CTCTokenizer
 
 warnings.filterwarnings("ignore")
@@ -11,22 +11,21 @@ warnings.filterwarnings("ignore")
 #load wav2vec2 tokenizer and model
 
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import time
+
 from fastapi import FastAPI, HTTPException, File
-model_name = "Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm"
+
 from transformers import pipeline
-#feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
-#tokenizer = AutoTokenizer.from_pretrained(model_name)
-#model = Wav2Vec2ForCTC.from_pretrained(model_name)
-pipe = pipeline(model="Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2",chunk_length_s=20, stride_length_s=(4, 2))
 
 
+pipe = pipeline(model="Finnish-NLP/wav2vec2-xlsr-1b-finnish-lm-v2",chunk_length_s=20, stride_length_s=(4, 2))
+
+token = os.getenv()
 
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model_checkpoint = 'Finnish-NLP/t5x-small-nl24-finnish'    
-tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_auth_token=True)
-model = AutoModelForSeq2SeqLM.from_pretrained('model_t5', from_flax=False, torch_dtype=torch.float32).to(device)
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_auth_token=os.environ.get('hf_token'))
+model = AutoModelForSeq2SeqLM.from_pretrained('Finnish-NLP/case_correction_model', from_flax=False, torch_dtype=torch.float32, use_auth_token=os.environ.get('hf_token')).to(device)
 
 
 # define speech-to-text function