test gemma

app.py

@@ -16,16 +16,20 @@ import librosa
 import pyloudnorm as pyln
 
 # Load the models and tokenizers
-model1 = Wav2Vec2ForCTC.from_pretrained("ai4bharat/indicwav2vec-hindi")
-tokenizer1 = Wav2Vec2Tokenizer.from_pretrained("ai4bharat/indicwav2vec-hindi")
+# model1 = Wav2Vec2ForCTC.from_pretrained("ai4bharat/indicwav2vec-hindi")
+# tokenizer1 = Wav2Vec2Tokenizer.from_pretrained("ai4bharat/indicwav2vec-hindi")
+model1 = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h")
+tokenizer1 = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-large-960h")
 # Loading the tokenizer and model from Hugging Face's model hub.
-tokenizer = AutoTokenizer.from_pretrained("soketlabs/pragna-1b", token=os.environ.get('HF_TOKEN'))
-model = AutoModelForCausalLM.from_pretrained(
-    "soketlabs/pragna-1b", 
-    token=os.environ.get('HF_TOKEN'),
-    revision='3c5b8b1309f7d89710331ba2f164570608af0de7'
-)
-model.load_adapter('soketlabs/pragna-1b-it-v0.1', token=os.environ.get('HF_TOKEN'))
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", token=os.environ.get('HF_TOKEN'))
+# tokenizer = AutoTokenizer.from_pretrained("soketlabs/pragna-1b", token=os.environ.get('HF_TOKEN'))
+# model = AutoModelForCausalLM.from_pretrained(
+#     "soketlabs/pragna-1b", 
+#     token=os.environ.get('HF_TOKEN'),
+#     revision='3c5b8b1309f7d89710331ba2f164570608af0de7'
+# )
+# model.load_adapter('soketlabs/pragna-1b-it-v0.1', token=os.environ.get('HF_TOKEN'))
 
 # using CUDA for an optimal experience
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')