Upload app.py

app.py

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+import streamlit as st
+import datetime
+from transformers import pipeline
+import gradio as gr
+
+import tempfile
+from typing import Optional
+import numpy as np
+from TTS.utils.manage import ModelManager
+from TTS.utils.synthesizer import Synthesizer
+
+# PersistDataset -----
+import os
+import csv
+import gradio as gr
+from gradio import inputs, outputs
+import huggingface_hub
+from huggingface_hub import Repository, hf_hub_download, upload_file
+from datetime import datetime
+
+# created new dataset as awacke1/MindfulStory.csv
+DATASET_REPO_URL = "https://huggingface.co/datasets/awacke1/MindfulStory.csv"
+DATASET_REPO_ID = "awacke1/MindfulStory.csv"
+DATA_FILENAME = "MindfulStory.csv"
+DATA_FILE = os.path.join("data", DATA_FILENAME)
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+# Download dataset repo using hub download
+try:
+    hf_hub_download(
+        repo_id=DATASET_REPO_ID,
+        filename=DATA_FILENAME,
+        cache_dir=DATA_DIRNAME,
+        force_filename=DATA_FILENAME
+    )
+except:
+    print("file not found")
+    
+def AIMemory(name: str, message: str):
+    if name and message:
+        with open(DATA_FILE, "a") as csvfile:
+            writer = csv.DictWriter(csvfile, fieldnames=["name", "message", "time"])
+            writer.writerow({"name": name, "message": message, "time": str(datetime.now())})
+        commit_url = repo.push_to_hub()
+    return {"name": name, "message": message, "time": str(datetime.now())}
+
+with open('Mindfulness.txt', 'r') as file:
+        context = file.read()
+   
+# Set up cloned dataset from repo for operations
+repo = Repository( local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN)
+
+# set up ASR
+asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
+
+# set up TTS
+MODEL_NAMES = [
+    "en/ljspeech/tacotron2-DDC",
+    "en/ljspeech/glow-tts",
+    "en/ljspeech/speedy-speech-wn",
+    "en/ljspeech/vits",
+    "en/sam/tacotron-DDC",
+    "fr/mai/tacotron2-DDC",
+    "de/thorsten/tacotron2-DCA",
+]
+
+# Use Model Manager to load vocoders
+MODELS = {}
+manager = ModelManager()
+for MODEL_NAME in MODEL_NAMES:
+    print(f"downloading {MODEL_NAME}")
+    model_path, config_path, model_item = manager.download_model(f"tts_models/{MODEL_NAME}")
+    vocoder_name: Optional[str] = model_item["default_vocoder"]
+    vocoder_path = None
+    vocoder_config_path = None
+    if vocoder_name is not None:
+        vocoder_path, vocoder_config_path, _ = manager.download_model(vocoder_name)
+
+    synthesizer = Synthesizer(
+        model_path, config_path, None, vocoder_path, vocoder_config_path,
+    )
+    MODELS[MODEL_NAME] = synthesizer
+
+# transcribe
+def transcribe(audio):
+    text = asr(audio)["text"]
+    return text
+
+#text classifier
+classifier = pipeline("text-classification")
+
+
+def speech_to_text(speech):
+    text = asr(speech)["text"]
+    #rMem = AIMemory("STT", text) 
+    return text
+
+def text_to_sentiment(text):
+    sentiment = classifier(text)[0]["label"]
+    #rMem = AIMemory(text, sentiment) 
+    return sentiment 
+
+def upsert(text):
+    date_time =str(datetime.datetime.today())
+    doc_ref = db.collection('Text2SpeechSentimentSave').document(date_time)
+    doc_ref.set({u'firefield': 'Recognize Speech', u'first': 'https://huggingface.co/spaces/awacke1/TTS-STT-Blocks/', u'last': text, u'born': date_time,})
+    saved = select('TTS-STT', date_time)
+    return saved
+      
+def select(collection, document):
+    doc_ref = db.collection(collection).document(document)
+    doc = doc_ref.get()
+    docid = ("The id is: ", doc.id)
+    contents = ("The contents are: ", doc.to_dict())
+    return contents
+          
+def selectall(text):
+    docs = db.collection('Text2SpeechSentimentSave').stream()
+    doclist=''
+    for doc in docs:
+        r=(f'{doc.id} => {doc.to_dict()}')
+        doclist += r
+    return doclist 
+    
+def tts(text: str, model_name: str):
+    print(text, model_name)
+    synthesizer = MODELS.get(model_name, None)
+    if synthesizer is None:
+        raise NameError("model not found")
+    wavs = synthesizer.tts(text)
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
+        synthesizer.save_wav(wavs, fp)
+        
+        #rMem = AIMemory("TTS", text + model_name) 
+    
+        return fp.name
+
+demo = gr.Blocks()
+with demo:
+    audio_file = gr.inputs.Audio(source="microphone", type="filepath")
+    text = gr.Textbox(label="Speech to Text")
+    #label = gr.Label()
+    #saved = gr.Textbox(label="Saved")
+    #savedAll = gr.Textbox(label="SavedAll")
+    TTSchoice = gr.inputs.Radio( label="Pick a Text to Speech Model", choices=MODEL_NAMES,   )
+    audio = gr.Audio(label="Output", interactive=False)
+    
+    b1 = gr.Button("Recognize Speech")
+    #b2 = gr.Button("Classify Sentiment")
+    #b3 = gr.Button("Save Speech to Text")
+    #b4 = gr.Button("Retrieve All")
+    b5 = gr.Button("Read It Back Aloud")
+
+    b1.click(speech_to_text, inputs=audio_file, outputs=text)
+    #b2.click(text_to_sentiment, inputs=text, outputs=label)
+    #b3.click(upsert, inputs=text, outputs=saved)
+    #b4.click(selectall, inputs=text, outputs=savedAll)
+    b5.click(tts,  inputs=[text,TTSchoice], outputs=audio)
+    
+demo.launch(share=True)