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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_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 generate_html() -> str:
+    with open(DATA_FILE) as csvfile:
+        reader = csv.DictReader(csvfile)
+        rows = []
+        for row in reader:
+            rows.append(row)
+        rows.reverse()
+        if len(rows) == 0:
+            return "no messages yet"
+        else:
+            html = "<div class='chatbot'>"
+            for row in rows:
+                html += "<div>"
+                html += f"<span>{row['inputs']}</span>"
+                html += f"<span class='outputs'>{row['outputs']}</span>"
+                html += "</div>"
+            html += "</div>"
+            return html
+            
+            
+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())}
+            )
+        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})",
+)
+
+
+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
+    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 = "", im4 = "", file = ""):
+#def transcribe(audio, state = "", im4 = None, file = None):
+def transcribe(audio, state = ""):    # two parms - had been testing video and file inputs at same time.
+    # Grant additional context
+    # time.sleep(1)
+    if state is None:
+        state = ""
+    audio_data = process_audio_file(audio)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # Filepath transcribe
+        audio_path = os.path.join(tmpdir, f'audio_{uuid.uuid4()}.wav')
+        soundfile.write(audio_path, audio_data, SAMPLE_RATE)
+        transcriptions = model.transcribe([audio_path])
+       # Direct transcribe
+        # transcriptions = model.transcribe([audio])
+        # if transcriptions form a tuple (from RNNT), extract just "best" hypothesis
+        if type(transcriptions) == tuple and len(transcriptions) == 2:
+            transcriptions = transcriptions[0]
+        transcriptions = transcriptions[0]
+    store_message(transcriptions, state) # Save to dataset
+    state = state + transcriptions + " "
+    return state, state
+
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(source="microphone", type='filepath', streaming=True),
+        "state",
+        #gr.Image(label="Webcam", source="webcam"),
+        #gr.File(label="File"),
+    ],
+    outputs=[
+        "textbox",
+        "state",
+        #gr.HighlightedText(label="HighlightedText", color_map={"punc": "pink", "test 0": "blue"}),
+        #gr.HighlightedText(label="HighlightedText", show_legend=True),
+        #gr.JSON(label="JSON"),
+        #gr.HTML(label="HTML"),
+    ],
+    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})"
+)
+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