Rename temp_store to temp_store.py

temp_store.py

@@ -0,0 +1,60 @@
+
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, VitsModel
+from nemo.collections.asr.models import EncDecMultiTaskModel
+
+
+# load speech to text model
+canary_model = EncDecMultiTaskModel.from_pretrained('nvidia/canary-1b')
+canary_model.eval()
+canary_model.to('cpu')
+
+# update decode params
+canary_model.change_decoding_strategy(None)
+decode_cfg = canary_model.cfg.decoding
+decode_cfg.beam.beam_size = 1
+canary_model.change_decoding_strategy(decode_cfg)
+
+
+
+
+# Load the text processing model and tokenizer
+proc_tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
+proc_model = AutoModelForCausalLM.from_pretrained(
+    "microsoft/Phi-3-mini-4k-instruct",
+    trust_remote_code=True,
+)
+proc_model.eval()
+proc_model.to('cpu')
+
+
+# Load the TTS model
+tts_model = VitsModel.from_pretrained("facebook/mms-tts-eng")
+tts_tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng")
+tts_model.eval()
+tts_model.to('cpu')
+
+
+def process_speech(speech):
+    # Convert the speech to text
+    transcription = canary_model.transcribe(
+        speech, 
+        logprobs=False,
+    )
+
+    # Process the text
+    inputs = proc_tokenizer.encode(transcription + proc_tokenizer.eos_token, return_tensors='pt')
+    outputs = proc_model.generate(inputs, max_length=100, temperature=0.7, pad_token_id=proc_tokenizer.eos_token_id)
+    text = proc_tokenizer.decode(outputs[0], skip_special_tokens=True)
+    processed_text = tts_tokenizer(text, return_tensors="pt")
+    
+    # Convert the processed text to speech
+    with torch.no_grad():
+        audio = tts_model(**inputs).waveform   
+
+    return audio
+
+iface = gr.Interface(fn=process_speech, inputs=gr.inputs.Audio(source="microphone"), outputs="audio")
+
+iface.launch()