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# Get Transcription, WER and PPM
"""
TODO:
[DONE]: Automatic generating Config
"""
import yaml
import argparse
import sys
from pathlib import Path
sys.path.append("./src")
import lightning_module
from UV import plot_UV, get_speech_interval
from transformers import pipeline
from rich.progress import track
from rich import print as rprint
import numpy as np
import jiwer
import pdb
import torch.nn as nn
import torch
import torchaudio
import gradio as gr
from sys import flags
from random import sample
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
# root_path = Path(__file__).parents[1]
class ChangeSampleRate(nn.Module):
def __init__(self, input_rate: int, output_rate: int):
super().__init__()
self.output_rate = output_rate
self.input_rate = input_rate
def forward(self, wav: torch.tensor) -> torch.tensor:
# Only accepts 1-channel waveform input
wav = wav.view(wav.size(0), -1)
new_length = wav.size(-1) * self.output_rate // self.input_rate
indices = torch.arange(new_length) * (
self.input_rate / self.output_rate
)
round_down = wav[:, indices.long()]
round_up = wav[:, (indices.long() + 1).clamp(max=wav.size(-1) - 1)]
output = round_down * (1.0 - indices.fmod(1.0)).unsqueeze(
0
) + round_up * indices.fmod(1.0).unsqueeze(0)
return output
model = lightning_module.BaselineLightningModule.load_from_checkpoint(
"./src/epoch=3-step=7459.ckpt"
).eval()
def calc_mos(audio_path, ref):
wav, sr = torchaudio.load(audio_path)
osr = 16_000
batch = wav.unsqueeze(0).repeat(10, 1, 1)
csr = ChangeSampleRate(sr, osr)
out_wavs = csr(wav)
# ASR
trans = p(audio_path)["text"]
# WER
wer = jiwer.wer(
ref,
trans,
truth_transform=transformation,
hypothesis_transform=transformation,
)
# MOS
batch = {
"wav": out_wavs,
"domains": torch.tensor([0]),
"judge_id": torch.tensor([288]),
}
with torch.no_grad():
output = model(batch)
predic_mos = output.mean(dim=1).squeeze().detach().numpy() * 2 + 3
# Phonemes per minute (PPM)
with torch.no_grad():
logits = phoneme_model(out_wavs).logits
phone_predicted_ids = torch.argmax(logits, dim=-1)
phone_transcription = processor.batch_decode(phone_predicted_ids)
lst_phonemes = phone_transcription[0].split(" ")
wav_vad = torchaudio.functional.vad(wav, sample_rate=sr)
ppm = len(lst_phonemes) / (wav_vad.shape[-1] / sr) * 60
# if float(predic_mos) >= 3.0:
# torchaudio.save("good.wav", wav,sr)
return predic_mos, trans, wer, phone_transcription, ppm
if __name__ == "__main__":
# Argparse
parser = argparse.ArgumentParser(
prog="get_ref_PPM",
description="Generate Phoneme per Minute (and Voice/Unvoice plot)",
epilog="",
)
parser.add_argument(
"--tag",
type=str,
default=None,
required=False,
help="ID tag for output *.csv",
)
parser.add_argument("--ref_txt", type=str, required=True, help="Reference TXT")
parser.add_argument(
"--ref_wavs", type=str, required=True, help="Reference WAVs"
)
parser.add_argument(
"--output_dir",
type=str,
required=True,
help="Output Directory for *.csv",
)
parser.add_argument(
"--to_config",
choices=["True", "False"],
default="False",
help="Generating Config from .txt and wavs/*wav",
)
parser.add_argument(
"--UV_flag",
choices=["True", "False"],
default="False",
help="Toggle for U/V plot",
)
parser.add_argument(
"--UV_thre", type=float, default=40, help="U/V threshold dB"
)
args = parser.parse_args()
refs = np.loadtxt(args.ref_txt, delimiter="\n", dtype="str")
refs_ids = [x.split()[0] for x in refs]
refs_txt = [" ".join(x.split()[1:]) for x in refs]
ref_wavs = [str(x) for x in sorted(Path(args.ref_wavs).glob("**/*.wav"))]
# pdb.set_trace()
try:
len(refs) == len(ref_wavs)
except ValueError:
print("Error: Text and Wavs don't match")
exit()
# ASR part
p = pipeline("automatic-speech-recognition")
# WER part
transformation = jiwer.Compose(
[
jiwer.ToLowerCase(),
jiwer.RemoveWhiteSpace(replace_by_space=True),
jiwer.RemoveMultipleSpaces(),
jiwer.ReduceToListOfListOfWords(word_delimiter=" "),
]
)
# WPM part
processor = Wav2Vec2Processor.from_pretrained(
"facebook/wav2vec2-xlsr-53-espeak-cv-ft"
)
phoneme_model = Wav2Vec2ForCTC.from_pretrained(
"facebook/wav2vec2-xlsr-53-espeak-cv-ft"
)
# phoneme_model = pipeline(model="facebook/wav2vec2-xlsr-53-espeak-cv-ft")
description = """
MOS prediction demo using UTMOS-strong w/o phoneme encoder model, \
which is trained on the main track dataset.
This demo only accepts .wav format. Best at 16 kHz sampling rate.
Paper is available [here](https://arxiv.org/abs/2204.02152)
Add ASR based on wav2vec-960, currently only English available.
Add WER interface.
"""
referance_id = gr.Textbox(
value="ID", placeholder="Utter ID", label="Reference_ID"
)
referance_textbox = gr.Textbox(
value="", placeholder="Input reference here", label="Reference"
)
# Set up interface
result = []
result.append("id, pred_mos, trans, wer, pred_phone, ppm")
if args.UV_flag == "False":
for id, x, y in track(
zip(refs_ids, ref_wavs, refs_txt),
total=len(refs_ids),
description="Loading references information",
):
predic_mos, trans, wer, phone_transcription, ppm = calc_mos(x, y)
record = ",".join(
[
id,
str(predic_mos),
str(trans),
str(wer),
str(phone_transcription),
str(ppm),
]
)
result.append(record)
elif args.UV_flag == "True":
fig_tardir = Path(args.ref_wavs) / Path("PPM_figs")
Path.mkdir(Path(args.ref_wavs) / Path("PPM_figs"), exist_ok=True)
for id, x, y in track(
zip(refs_ids, ref_wavs, refs_txt),
total=len(refs_ids),
description="Loading references information",
):
# UV ploting
wav, sr = torchaudio.load(x)
wav_vad = torchaudio.functional.vad(wav, sample_rate=sr)
a_h, p_h = get_speech_interval(wav_vad.numpy(), db=args.UV_thre)
fig_h = plot_UV(wav_vad.numpy().squeeze(), a_h, sr=sr)
fig_h.savefig(Path(fig_tardir) / Path(id + ".png"), dpi=200)
# Acoustic calculation
predic_mos, trans, wer, phone_transcription, ppm = calc_mos(x, y)
record = ",".join(
[
id,
str(predic_mos),
str(trans),
str(wer),
str(phone_transcription),
str(ppm),
]
)
result.append(record)
# Output
if args.tag == None:
args.tag = Path(args.ref_wavs).stem
# Make output_dir
# pdb.set_trace()
Path.mkdir(Path(args.output_dir), exist_ok=True)
# pdb.set_trace()
with open("%s/%s.csv" % (args.output_dir, args.tag), "w") as f:
print("\n".join(result), file=f)
# Generating config
if args.to_config == "True":
config_dict = {
"exp_id": args.tag,
"ref_txt": args.ref_txt,
"ref_feature": "%s/%s.csv" % (args.output_dir, args.tag),
"ref_wavs": args.ref_wavs,
"thre": {
"minppm": 100,
"maxppm": 100,
"WER": 0.1,
"AUTOMOS": 4.0,
},
"auth": {"username": None, "password": None},
}
with open("./config/%s.yaml" % args.tag, "w") as config_f:
rprint("Dumping as config ./config/%s.yaml" % args.tag)
rprint(config_dict)
yaml.dump(config_dict, stream=config_f)
rprint("Change parameter ./config/%s.yaml if necessary" % args.tag)
print("Reference Dumping Finished") |