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app.py

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+import gradio as gr
+import torch
+import time
+import librosa
+import soundfile
+import nemo.collections.asr as nemo_asr
+import tempfile
+import os
+import uuid
+
+from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
+import torch
+
+# 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
+
+# ---------------------------------------------
+# Dataset and Token links - change awacke1 to your own HF id, and add a HF_TOKEN copy to your repo for write permissions
+# This should allow you to save your results to your own Dataset hosted on HF. ---
+#DATASET_REPO_URL = "https://huggingface.co/datasets/awacke1/Carddata.csv"
+#DATASET_REPO_ID = "awacke1/Carddata.csv"
+#DATA_FILENAME = "Carddata.csv"
+#DATA_FILE = os.path.join("data", DATA_FILENAME)
+#HF_TOKEN = os.environ.get("HF_TOKEN")
+#SCRIPT = """
+
+#<script>
+#if (!window.hasBeenRun) {
+#    window.hasBeenRun = true;
+#    console.log("should only happen once");
+#    document.querySelector("button.submit").click();
+#}
+#</script>
+#"""
+
+#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")
+#repo = Repository(
+#    local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
+#)
+           
+#def store_message(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.strip(), "message": message.strip(), "time": str(datetime.now())}
+#            )
+#        # uncomment line below to begin saving - 
+#        commit_url = repo.push_to_hub()
+#    return ""            
+
+#iface = gr.Interface(
+#    store_message,
+#    [
+#        inputs.Textbox(placeholder="Your name"),
+#        inputs.Textbox(placeholder="Your message", lines=2),
+#    ],
+#    "html",
+#    css="""
+#    .message {background-color:cornflowerblue;color:white; padding:4px;margin:4px;border-radius:4px; }
+#    """,
+#    title="Reading/writing to a HuggingFace dataset repo from Spaces",
+#    description=f"This is a demo of how to do simple *shared data persistence* in a Gradio Space, backed by a dataset repo.",
+#    article=f"The dataset repo is [{DATASET_REPO_URL}]({DATASET_REPO_URL})",
+#)
+
+
+# main -------------------------
+mname = "facebook/blenderbot-400M-distill"
+model = BlenderbotForConditionalGeneration.from_pretrained(mname)
+tokenizer = BlenderbotTokenizer.from_pretrained(mname)
+
+def take_last_tokens(inputs, note_history, history):
+    """Filter the last 128 tokens"""
+    if inputs['input_ids'].shape[1] > 128:
+        inputs['input_ids'] = torch.tensor([inputs['input_ids'][0][-128:].tolist()])
+        inputs['attention_mask'] = torch.tensor([inputs['attention_mask'][0][-128:].tolist()])
+        note_history = ['</s> <s>'.join(note_history[0].split('</s> <s>')[2:])]
+        history = history[1:]
+    return inputs, note_history, history
+
+def add_note_to_history(note, note_history):
+    """Add a note to the historical information"""
+    note_history.append(note)
+    note_history = '</s> <s>'.join(note_history)
+    return [note_history]
+
+
+def chat(message, history):
+    history = history or []
+    if history: 
+        history_useful = ['</s> <s>'.join([str(a[0])+'</s> <s>'+str(a[1]) for a in history])]
+    else:
+        history_useful = []
+    history_useful = add_note_to_history(message, history_useful)
+    inputs = tokenizer(history_useful, return_tensors="pt")
+    inputs, history_useful, history = take_last_tokens(inputs, history_useful, history)
+    reply_ids = model.generate(**inputs)
+    response = tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]
+    history_useful = add_note_to_history(response, history_useful)
+    list_history = history_useful[0].split('</s> <s>')
+    history.append((list_history[-2], list_history[-1]))
+#    store_message(message, response) # Save to dataset - uncomment if you uncomment above to save inputs and outputs to your dataset
+    return history, history
+    
+
+SAMPLE_RATE = 16000
+model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_en_conformer_transducer_xlarge")
+model.change_decoding_strategy(None)
+model.eval()
+
+def process_audio_file(file):
+    data, sr = librosa.load(file)
+    if sr != SAMPLE_RATE:
+        data = librosa.resample(data, orig_sr=sr, target_sr=SAMPLE_RATE)
+    # monochannel
+    data = librosa.to_mono(data)
+    return data
+
+
+def transcribe(audio, state = ""):   
+    if state is None:
+        state = ""
+    audio_data = process_audio_file(audio)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        audio_path = os.path.join(tmpdir, f'audio_{uuid.uuid4()}.wav')
+        soundfile.write(audio_path, audio_data, SAMPLE_RATE)
+        transcriptions = model.transcribe([audio_path])
+        if type(transcriptions) == tuple and len(transcriptions) == 2:
+            transcriptions = transcriptions[0]
+        transcriptions = transcriptions[0]
+#    store_message(transcriptions, state) # Save to dataset - uncomment to store into a dataset - hint you will need your HF_TOKEN
+    state = state + transcriptions + " "
+    return state, state
+
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(source="microphone", type='filepath', streaming=True),
+        "state",
+    ],
+    outputs=[
+        "textbox",
+        "state",
+    ],
+    layout="horizontal",
+    theme="huggingface",
+    title="🗣️LiveSpeechRecognition🧠Memory💾",
+    description=f"Live Automatic Speech Recognition (ASR) with Memory💾 Dataset.",
+    allow_flagging='never',
+    live=True,    
+#    article=f"Result Output Saved to Memory💾 Dataset: [{DATASET_REPO_URL}]({DATASET_REPO_URL})"
+    article=f"Important Videos to understanding AI and NLP Clinical Terminology, Assessment, and Value Based Care AI include Huggingfaces Course Series here: https://www.youtube.com/c/HuggingFace , AI NLP Innovations in 2022 for Clinical and Mental Health Care here: https://www.youtube.com/watch?v=r38lXjz3g6M&list=PLHgX2IExbFov_5_4WfkesR7gnWPHHG-a1  and this link to see and manage playlist here: https://www.youtube.com/playlist?list=PLHgX2IExbFov_5_4WfkesR7gnWPHHG-a1  Review at your leisure to understand AI and NLP impact to helping the world develop Clinical systems of the future using AI and NLP for Clinical Terminology and alignment to worldwide Value Based Care objectives to help people be healthy."
+)
+iface.launch()

packages.txt

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+ffmpeg
+libsndfile1

requirements.txt

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+nemo_toolkit[asr]
+transformers
+torch
+gradio
+Werkzeug
+huggingface_hub
+Pillow