commit

Mindfulness.txt

@@ -0,0 +1,42 @@
+Mindful Path to Health and Happiness
+Target	Path
+Stress	Break Cycle of Stress and Anxiety
+Stress	Feel Calm in Stressful Situations
+Stress	Deal with Work Pressure
+Stress	Learn to Reduce Feelings of Overwhelmed, Burned Out, Exhausted
+Stress	1) Mental Body Scan. 2) Stretch, Calm, Breath. 3) Relaxed Seat Breath. 4) Walk Feel.
+Stress	Brain gamification - Health trackers only if alleviating stress or helping breathing, satisfaction
+Pain	Relieve Stress, Build Support
+Pain	1) Relaxation Response. 2) Deep Breaths. 3) CBT- ID, Delete Not Helpful Thoughts, Strengthen Helpful. 4) Reprogram Pain Stress Reactions
+Sleep	Sleep Better and Find Joy
+Sleep	Yoga Sleep: 1) All Fours, Back Up Down. 2) Kneel Arms Overhead Down. 3) Feet Lift and Lower. 4) Side Hip Knee Raise. 5) Flat on Back
+Goals	Being a Happier and Healthier Person
+Pain	Relieve Pain
+Happiness	Learn to Use Mindfulness to Affect Well Being
+Actions	Build and Boost Mental Strength
+Actions	Spending Time Outdoors
+Actions	Daily Routine Tasks
+Actions	Eating and Drinking - Find Healthy Nutrition Habits
+Actions	Drinking - Find Reasons and Cut Back or Quit Entirely
+Actions	Niksen, Do Nothing, To Just Be, Idle, Hang Around, Look Around, Listen Music, Without Purpose
+Actions	Tai Chi
+Actions	Yoga and Meditation
+Actions	Take a Brain Break - Let Old Brain Amygdala Which Affects Feelings and Emotions, Settle and Be Quiet
+Actions	Learn with Neocortex Tools and Techniques to Strengthen Attention
+Actions	Infuse Mindfulness into Easy Daily Activities
+Actions	1) Sit. 2) Just Breath. 3) Thoughts Come, Let Go, Breath. 4) Wandering, Let Go, Breath. 5) Note Your Attention Strength Build
+Actions	1) Gratitude. 2) Escape Comfort Zone. 3) Be Alone w Thoughts. 4) Self Compassion. 5) Assert Power. 6) Label Emotions. 7) Use Mind Wisely
+Actions	1) Walk with Me. 2) Reflect on Day. 3) Learn to Meditate.
+Actions	Work Smarter: 1) Cut Yourself Slack. 2) Set Goals. 3) Take Breaks. 4) Just Breath. 5) One Thing at a Time. 6) Schedule.
+Actions	Mental Strength: 1) Your Friend You Talk. 2) Affirmation Mantra. 3) Future Self. 4) Name Feelings. 5) Coping Methods. 6) First Smart Step
+Physical	Practices to Remedy Physical and Emotional Issues
+Physical	Learn Minds Ability to Naturally Heal
+Physical	Feel Better Physically
+Physical	Manage Blood Sugar Levels Affecting Weight Gain and Muscles
+Physical	Manage Heart and Cardiovascular Risk
+Practice	Ways to Be Mindful Each Day
+Practice	Define and Try Your Best Version of Meditation
+Practice	Self Care and Community Care - Authentic Connection in RL and Online
+Practice	1) Stay Social. 2) Be Neighborly. 3) Hobbies. 4) Exercise. 5) Be Kinder than Necessary. 6) Inhale, Exhale.
+Practice	Learning and Memory: 1) Work your Brain Muscle. 2) Delete and Clear Distractions. 3) Minimize Switching Attention Residue. 4) Completion and Save Points.
+Practice	Focus: 1) Attentiveness. 2) Learning. 3) Mindfulness.

README.md

@@ -1,12 +1,13 @@
 ---
-title: TTS STT Blocks
-emoji: 👀
-colorFrom: green
+title: 🗣️SpeakUp🙉 - NLP Speech 2 Text 2 Speech Generator AI Pipeline 
+emoji: 🗣️🎤🙉
+colorFrom: red
 colorTo: purple
 sdk: gradio
-sdk_version: 3.13.0
+sdk_version: 3.0.11
 app_file: app.py
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

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)

gitattributes.txt

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

requirements.txt

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+transformers
+streamlit
+Werkzeug==2.0.3
+huggingface_hub==0.4.0
+torch
+TTS==0.2.1

test.json

@@ -0,0 +1,12 @@
+{
+  "type": "service_account",
+  "project_id": "clinical-nlp-b9117",
+  "private_key_id": "6972d02311e8ee0c5b582551fbcf9c99b9169b58",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\nh8qPMwuMcQ/vKGhl3xLYo9M=\n-----END PRIVATE KEY-----\n",
+  "client_email": "firebase-adminsdk-qaxaj@clinical-nlp-b9117.iam.gserviceaccount.com",
+  "client_id": "117623958723912081118",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/firebase-adminsdk-qaxaj%40clinical-nlp-b9117.iam.gserviceaccount.com"
+}