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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})" | |
| ) | |
| iface.launch() | |