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from typing import Tuple
import numpy as np
from espnet2.sds.utils.utils import int2float
def TTS_psuedomos(TTS_audio_output: Tuple[int, np.ndarray]) -> str:
"""
Compute and return speech quality metrics
for the given synthesized audio output
using the Versa library.
Args:
TTS_audio_output (Tuple[int, np.ndarray]):
A tuple containing:
- The first element (int): The frame rate of the audio.
- The second element (np.ndarray): The audio signal,
typically a NumPy array.
Returns:
str:
A formatted string containing each metric name
and its corresponding score, for example:
utmos: 3.54
dnsmos: 3.47
plcmos: 3.62
sheet_ssqa: 4.03
Raises:
ImportError:
If the Versa library is not installed or cannot be imported.
Example:
>>> tts_audio_output = (16000, audio_array)
>>> result = TTS_psuedomos(tts_audio_output)
>>> print(result)
utmos: 3.54
dnsmos: 3.47
plcmos: 3.62
sheet_ssqa: 4.03
"""
try:
from versa import (
pseudo_mos_metric,
pseudo_mos_setup,
sheet_ssqa,
sheet_ssqa_setup,
)
except Exception as e:
print("Error: Versa is not properly installed.")
raise e
predictor_dict, predictor_fs = pseudo_mos_setup(
use_gpu=True,
predictor_types=["utmos", "dnsmos", "plcmos"],
predictor_args={
"utmos": {"fs": 16000},
"dnsmos": {"fs": 16000},
"plcmos": {"fs": 16000},
},
)
score_modules = {
"module": pseudo_mos_metric,
"args": {
"predictor_dict": predictor_dict,
"predictor_fs": predictor_fs,
"use_gpu": True,
},
}
dict1 = score_modules["module"](
int2float(TTS_audio_output[1]),
TTS_audio_output[0],
**score_modules["args"],
)
str1 = ""
for k in dict1:
str1 = str1 + f"{k}: {dict1[k]:.2f}\n"
sheet_model = sheet_ssqa_setup(
model_tag="default",
model_path=None,
model_config=None,
use_gpu=True,
)
score_modules = {
"module": sheet_ssqa,
"args": {"model": sheet_model, "use_gpu": True},
}
dict1 = score_modules["module"](
score_modules["args"]["model"],
int2float(TTS_audio_output[1]),
TTS_audio_output[0],
use_gpu=score_modules["args"]["use_gpu"],
)
for k in dict1:
str1 = str1 + f"{k}: {dict1[k]:.2f}\n"
return str1
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