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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/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) | |