Spaces:
Runtime error
Runtime error
File size: 3,908 Bytes
65191fa d8d2ace 65191fa d8d2ace 65191fa d8d2ace 65191fa d8d2ace 65191fa d8d2ace 65191fa d8d2ace 65191fa d8d2ace 65191fa d8d2ace 65191fa ef7af0d d8d2ace 65191fa 1b3a760 65191fa d8d2ace 65191fa 95ff189 d8d2ace 65191fa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
import gradio as gr
import time
import torch
import scipy.io.wavfile
from espnet2.bin.tts_inference import Text2Speech
from espnet2.utils.types import str_or_none
from espnet2.bin.asr_inference import Speech2Text
# tagen = 'kan-bayashi/ljspeech_vits'
# vocoder_tagen = "none"
speech2text = Speech2Text.from_pretrained(
asr_train_config="slurp/config.yaml",
asr_model_file="slurp/valid.acc.ave_10best.pth",
# Decoding parameters are not included in the model file
nbest=1
)
# Confirm the sampling rate is equal to that of the training corpus.
# If not, you need to resample the audio data before inputting to speech2text
# speech, rate = soundfile.read("audio--1504190171-headset.flac")
# nbests = speech2text(speech)
# text, *_ = nbests[0]
# print(text)
# exit()
# text2speechen = Text2Speech.from_pretrained(
# model_tag=str_or_none(tagen),
# vocoder_tag=str_or_none(vocoder_tagen),
# device="cpu",
# # Only for Tacotron 2 & Transformer
# threshold=0.5,
# # Only for Tacotron 2
# minlenratio=0.0,
# maxlenratio=10.0,
# use_att_constraint=False,
# backward_window=1,
# forward_window=3,
# # Only for FastSpeech & FastSpeech2 & VITS
# speed_control_alpha=1.0,
# # Only for VITS
# noise_scale=0.333,
# noise_scale_dur=0.333,
# )
# tagjp = 'kan-bayashi/jsut_full_band_vits_prosody'
# vocoder_tagjp = 'none'
# text2speechjp = Text2Speech.from_pretrained(
# model_tag=str_or_none(tagjp),
# vocoder_tag=str_or_none(vocoder_tagjp),
# device="cpu",
# # Only for Tacotron 2 & Transformer
# threshold=0.5,
# # Only for Tacotron 2
# minlenratio=0.0,
# maxlenratio=10.0,
# use_att_constraint=False,
# backward_window=1,
# forward_window=3,
# # Only for FastSpeech & FastSpeech2 & VITS
# speed_control_alpha=1.0,
# # Only for VITS
# noise_scale=0.333,
# noise_scale_dur=0.333,
# )
# tagch = 'kan-bayashi/csmsc_full_band_vits'
# vocoder_tagch = "none"
# text2speechch = Text2Speech.from_pretrained(
# model_tag=str_or_none(tagch),
# vocoder_tag=str_or_none(vocoder_tagch),
# device="cpu",
# # Only for Tacotron 2 & Transformer
# threshold=0.5,
# # Only for Tacotron 2
# minlenratio=0.0,
# maxlenratio=10.0,
# use_att_constraint=False,
# backward_window=1,
# forward_window=3,
# # Only for FastSpeech & FastSpeech2 & VITS
# speed_control_alpha=1.0,
# # Only for VITS
# noise_scale=0.333,
# noise_scale_dur=0.333,
# )
def inference(wav,lang):
with torch.no_grad():
if lang == "english":
speech, rate = soundfile.read(wav.name)
nbests = speech2text(speech)
text, *_ = nbests[0]
# if lang == "chinese":
# wav = text2speechch(text)["wav"]
# scipy.io.wavfile.write("out.wav",text2speechch.fs , wav.view(-1).cpu().numpy())
# if lang == "japanese":
# wav = text2speechjp(text)["wav"]
# scipy.io.wavfile.write("out.wav",text2speechjp.fs , wav.view(-1).cpu().numpy())
return text
title = "ESPnet2-SLU"
description = "Gradio demo for ESPnet2-SLU: Extending the Edge of SLU Research. To use it, simply record your audio. Read more at the links below."
article = "<p style='text-align: center'><a href='https://github.com/espnet/espnet' target='_blank'>Github Repo</a></p>"
examples=[['audio_slurp.flac',"english"]]
# gr.inputs.Textbox(label="input text",lines=10),gr.inputs.Radio(choices=["english"], type="value", default="english", label="language")
gr.Interface(
inference,
[gr.inputs.Audio(label="input audio", type="file"),gr.inputs.Radio(choices=["english"], type="value", default="english", label="language")],
gr.outputs.Textbox(type="str", label="Output"),
title=title,
description=description,
article=article,
enable_queue=True,
examples=examples
).launch(debug=True)
|