Upload 4 files

README.md

@@ -1,13 +1,13 @@
 ---
-title: 2 GradioLiveASR
-emoji: 🌖
-colorFrom: blue
+title: 🗣️Live ASR Speech Recognition Gradio🧠💾 
+emoji: 🗣️Live🧠
+colorFrom: purple
 colorTo: red
 sdk: gradio
-sdk_version: 3.18.0
+sdk_version: 3.5
 app_file: app.py
 pinned: false
-license: mit
+license: apache-2.0
 ---
 
-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-config-reference

app.py

@@ -0,0 +1,138 @@
+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/ASRLive.csv"
+DATASET_REPO_ID = "awacke1/ASRLive.csv"
+DATA_FILENAME = "ASRLive.csv"
+DATA_FILE = os.path.join("data", DATA_FILENAME)
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+PersistToDataset = False
+#PersistToDataset = True  # uncomment to save inference output to ASRLive.csv dataset
+
+if PersistToDataset:
+    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()
+        ret = ""
+        with open(DATA_FILE, "r") as csvfile:
+            reader = csv.DictReader(csvfile)
+            
+            for row in reader:
+                ret += row
+                ret += "\r\n"
+    return ret            
+
+# main -------------------------
+mname = "facebook/blenderbot-400M-distill"
+model = BlenderbotForConditionalGeneration.from_pretrained(mname)
+tokenizer = BlenderbotTokenizer.from_pretrained(mname)
+
+def take_last_tokens(inputs, note_history, history):
+    filterTokenCount = 128 # filter last 128 tokens
+    if inputs['input_ids'].shape[1] > filterTokenCount:
+        inputs['input_ids'] = torch.tensor([inputs['input_ids'][0][-filterTokenCount:].tolist()])
+        inputs['attention_mask'] = torch.tensor([inputs['attention_mask'][0][-filterTokenCount:].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):
+    note_history.append(note)
+    note_history = '</s> <s>'.join(note_history)
+    return [note_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)
+    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]
+        
+    if PersistToDataset:
+        ret = store_message(transcriptions, state) # Save to dataset - uncomment to store into a dataset - hint you will need your HF_TOKEN
+        state = state + transcriptions + " " + ret
+    else:
+        state = state + transcriptions
+    return state, state
+
+gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(source="microphone", type='filepath', streaming=True),
+        "state",
+    ],
+    outputs=[
+        "textbox",
+        "state"
+    ],
+    layout="horizontal",
+    theme="huggingface",
+    title="🗣️ASR-Gradio-Live🧠💾",
+    description=f"Live Automatic Speech Recognition (ASR).",
+    allow_flagging='never',
+    live=True,    
+    article=f"Result💾 Dataset: [{DATASET_REPO_URL}]({DATASET_REPO_URL})"
+).launch(debug=True)

packages.txt

@@ -0,0 +1,2 @@
+ffmpeg
+libsndfile1

requirements.txt

@@ -0,0 +1,7 @@
+nemo_toolkit[asr]
+transformers
+torch
+gradio
+Werkzeug
+huggingface_hub
+Pillow