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| import gradio as gr | |
| import argparse | |
| import soundfile as sf | |
| import numpy as np | |
| import tempfile | |
| from pathlib import Path | |
| import os | |
| import subprocess | |
| import sys | |
| import re | |
| # from transformers import AutoProcessor, AutoModelForPreTraining | |
| # processor = AutoProcessor.from_pretrained("patrickvonplaten/mms-1b") | |
| # model = AutoModelForPreTraining.from_pretrained("patrickvonplaten/mms-1b") | |
| def process(audio, model, lang, format): | |
| with tempfile.TemporaryDirectory() as tmpdir: | |
| print(">>> preparing tmp manifest dir ...", file=sys.stderr) | |
| tmpdir = Path(tmpdir) | |
| with open(tmpdir / "dev.tsv", "w") as fw: | |
| fw.write("/\n") | |
| for audio in audio: | |
| nsample = sf.SoundFile(audio).frames | |
| fw.write(f"{audio}\t{nsample}\n") | |
| with open(tmpdir / "dev.uid", "w") as fw: | |
| fw.write(f"{audio}\n"*len(audio)) | |
| with open(tmpdir / "dev.ltr", "w") as fw: | |
| fw.write("d u m m y | d u m m y\n"*len(audio)) | |
| with open(tmpdir / "dev.wrd", "w") as fw: | |
| fw.write("dummy dummy\n"*len(audio)) | |
| cmd = f""" | |
| PYTHONPATH=. PREFIX=INFER HYDRA_FULL_ERROR=1 python infer.py -m decoding.type=viterbi dataset.max_tokens=4000000 distributed_training.distributed_world_size=1 "common_eval.path='{model}'" task.data={tmpdir} dataset.gen_subset="{lang}:dev" common_eval.post_process={format} decoding.results_path={tmpdir} | |
| """ | |
| print(">>> loading model & running inference ...", file=sys.stderr) | |
| subprocess.run(cmd, shell=True, stdout=subprocess.DEVNULL,) | |
| with open(tmpdir/"hypo.word") as fr: | |
| for ii, hypo in enumerate(fr): | |
| hypo = re.sub("\(\S+\)$", "", hypo).strip() | |
| print(f'===============\nInput: {audio[ii]}\nOutput: {hypo}') | |
| def transcribe(audio): | |
| model = "base_300m.pt" | |
| lang = "eng" | |
| format = "letter" | |
| process(np.ravel(audio), model, lang, format) | |
| gr.Interface( | |
| title = 'MetaAI (Facebook Research) MMS (Massively Multilingual Speech) ASR', | |
| fn=transcribe, | |
| inputs=[ | |
| gr.inputs.Audio(source="microphone", type="filepath") | |
| ], | |
| outputs=[ | |
| "textbox" | |
| ], | |
| live=True).launch() |