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app.py

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+
+import time
+
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModelForSeq2SeqLM
+
+import gradio as gr
+import speech_recognition as sr
+from math import log2, pow
+import os
+
+#from scipy.fftpack import fft
+import gc
+
+peft_model_id='hackathon-somos-nlp-2023/T5unami-small-v1'
+
+config = PeftConfig.from_pretrained(peft_model_id)
+model2 = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, return_dict=True, 
+                                               # load_in_8bit=True, 
+                                               # load_in_8bit_fp32_cpu_offload=True,
+                                               device_map='auto')
+tokenizer2 = AutoTokenizer.from_pretrained(peft_model_id)
+
+model2 = PeftModel.from_pretrained(model2, peft_model_id)
+
+Problema_tarjetaCredito= os.path.abspath("Problema_tarjetaCredito.ogg")
+list_audios= [[Problema_tarjetaCredito]]
+
+def gen_conversation(text,max_new_tokens=100): 
+  text = "<SN>instruction: " + text + "\n "
+  batch = tokenizer2(text, return_tensors='pt')
+  
+  output_tokens = model2.generate(**batch, 
+                                    max_new_tokens=max_new_tokens, 
+                                    eos_token_id= tokenizer2.eos_token_id,
+                                    pad_token_id= tokenizer2.pad_token_id,
+                                    bos_token_id= tokenizer2.bos_token_id,
+                                    early_stopping = True, 
+                                    no_repeat_ngram_size=2,
+                                    repetition_penalty=1.2,
+                                    temperature=.9,
+                                    num_beams=3
+                                    )
+  gc.collect()
+  return tokenizer2.decode(output_tokens[0], skip_special_tokens=True).split("\n")[-1].replace("output:","")
+
+conversacion = ""
+def speech_to_text(audio_file, texto_adicional):
+    global conversacion
+    if audio_file is not None:
+        # Lógica para entrada de audio
+        r = sr.Recognizer()
+        audio_data = sr.AudioFile(audio_file)
+        with audio_data as source:
+            audio = r.record(source)
+        text_enrada=""
+        
+        texto_generado = r.recognize_google(audio, language="es-ES")
+        texto_generado= f"[|Audio a texto|]:{texto_generado}\n" + "<br>[AGENTE]:"+gen_conversation(texto_generado,max_new_tokens=500)
+        texto_generado = "<div style='color: #66b3ff;'>" + texto_generado + "</div><br>"
+    else:
+        texto_generado= f"[|Solo texto|]:{texto_adicional}\n" + "<br>[AGENTE]:"+gen_conversation(texto_adicional,max_new_tokens=500)
+        texto_generado = "<div style='color: #66b3ff;'> " + texto_generado + "</div><br>"
+    conversacion += texto_generado
+    return conversacion
+
+iface = gr.Interface(
+    fn=speech_to_text,
+    inputs=[gr.inputs.Audio(label="Voz", type="filepath"), gr.inputs.Textbox(label="Texto adicional")],
+    outputs=gr.outputs.HTML(label=["chatbot","state"]),
+    title="Chat bot para empresas.",
+    description="Este modelo convierte la entrada de voz o texto y hace inferencia",
+    examples=list_audios,
+    theme="default",
+    layout="vertical",
+    allow_flagging=False,
+    flagging_dir=None,
+    server_name=None,
+    server_port=None,
+    live=False,
+    capture_session=False
+)
+
+iface.launch()
+
+

requirements.txt

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+transformers
+torch
+SpeechRecognition
+git+https://github.com/huggingface/peft.git
+gradio
+bitsandbytes
+loralib