diff --git a/.gitattributes b/.gitattributes index 004781588fd029cd2b8fc72ac397357d84ede5f8..82850d2c356b4ac4e441ce443482a3d34f39fa7d 100644 --- a/.gitattributes +++ b/.gitattributes @@ -46,18 +46,28 @@ static/videos/shiba.mp4 filter=lfs diff=lfs merge=lfs -text static/videos/steve.mp4 filter=lfs diff=lfs merge=lfs -text static/videos/teaser.mp4 filter=lfs diff=lfs merge=lfs -text static/videos/toby.mp4 filter=lfs diff=lfs merge=lfs -text -assets/mels filter=lfs diff=lfs merge=lfs -text -english_demo filter=lfs diff=lfs merge=lfs -text -multilingual_demo/** filter=lfs diff=lfs merge=lfs -text + +# Track common binary assets for the demo +*.wav filter=lfs diff=lfs merge=lfs -text +*.mp3 filter=lfs diff=lfs merge=lfs -text +*.flac filter=lfs diff=lfs merge=lfs -text +*.ogg filter=lfs diff=lfs merge=lfs -text +*.m4a filter=lfs diff=lfs merge=lfs -text +*.aac filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text +*.jpg filter=lfs diff=lfs merge=lfs -text +*.jpeg filter=lfs diff=lfs merge=lfs -text +*.webp filter=lfs diff=lfs merge=lfs -text .wav filter=lfs diff=lfs merge=lfs -text .png filter=lfs diff=lfs merge=lfs -text -.jpg filter=lfs diff=lfs merge=lfs -text -.jpeg filter=lfs diff=lfs merge=lfs -text -.webp filter=lfs diff=lfs merge=lfs -text .mp3 filter=lfs diff=lfs merge=lfs -text .flac filter=lfs diff=lfs merge=lfs -text .ogg filter=lfs diff=lfs merge=lfs -text .m4a filter=lfs diff=lfs merge=lfs -text .aac filter=lfs diff=lfs merge=lfs -text -*.png filter=lfs diff=lfs merge=lfs -text -*.wav filter=lfs diff=lfs merge=lfs -text +.jpg filter=lfs diff=lfs merge=lfs -text +.jpeg filter=lfs diff=lfs merge=lfs -text +.webp filter=lfs diff=lfs merge=lfs -text +assets/mels filter=lfs diff=lfs merge=lfs -text +english_demo filter=lfs diff=lfs merge=lfs -text +multilingual_demo/** filter=lfs diff=lfs merge=lfs -text diff --git a/.python-version b/.python-version new file mode 100644 index 0000000000000000000000000000000000000000..24ee5b1be9961e38a503c8e764b7385dbb6ba124 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/README.md b/README.md index 772c8e8c6d061964913a8ba0ea9a8df018c51ec1..9bb3c6dd605959e0fe2f9c083c440b49853f5457 100644 --- a/README.md +++ b/README.md @@ -9,19 +9,19 @@ license: mit short_description: Demo page for Sidon speech restoration model --- -# Nerfies +## Usage -This is the repository that contains source code for the [Nerfies website](https://nerfies.github.io). +- Add your audio files to `assets/audio/`: + - `assets/audio/sample1_noisy.wav` + - `assets/audio/sample1_restored.wav` +- Generate mel-spectrogram images (optional helper in `tools/`): + - Install deps: `pip install librosa matplotlib` + - Create images: + - `python tools/make_mel_images.py assets/audio/sample1_noisy.wav assets/mels/sample1_noisy.png` + - `python tools/make_mel_images.py assets/audio/sample1_restored.wav assets/mels/sample1_restored.png` +- Open `index.html` and duplicate one of the “Sample” boxes to add more items. Update the file paths per sample. -If you find Nerfies useful for your work please cite: -``` -@article{park2021nerfies - author = {Park, Keunhong and Sinha, Utkarsh and Barron, Jonathan T. and Bouaziz, Sofien and Goldman, Dan B and Seitz, Steven M. and Martin-Brualla, Ricardo}, - title = {Nerfies: Deformable Neural Radiance Fields}, - journal = {ICCV}, - year = {2021}, -} -``` +The page uses Bulma CSS and standard HTML5 audio elements, so no backend is required. Serve statically (e.g., `python -m http.server`). # Website License Creative Commons License
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index 0000000000000000000000000000000000000000..33fc643eac9720a8db40b271747c942581e580d8 --- /dev/null +++ b/english_demo/sidon/6930_76324_000020_000000.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:363e6d0547a97820dfc3cb56b1592b803e7b0fcf572efe1958f479cef15ad39c +size 143786 diff --git a/full_result.html b/full_result.html new file mode 100644 index 0000000000000000000000000000000000000000..5253d696d90f865895806474adb86c2dac883caf --- /dev/null +++ b/full_result.html @@ -0,0 +1,2299 @@ + + + + + + + + Sidon • FLEURS Multilingual Evaluation Results + + + + + + + + + +
+
+
+

Results Table

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
CERDNSMOSDNSMOS_95%_CINISQANISQA_95%_CIspk_sim
noisymiiphersidonnoisymiiphersidonnoisymiiphersidonnoisymiiphersidonnoisymiiphersidonnoisymiiphersidon
lang
Total0.0840.0940.0902.9103.3523.3930.0030.0010.0013.2524.4754.4200.0050.0020.0021.0000.9790.979
af_za0.0730.0800.0772.8913.3303.3520.0530.0190.0153.4424.5794.5440.0840.0150.0191.0000.9850.984
am_et0.0840.0940.0922.9443.3083.3690.0300.0120.0093.3894.5414.5370.0340.0140.0131.0000.9910.990
ar_eg0.0740.0800.0773.1163.3943.4190.0290.0120.0103.4494.5254.5200.0560.0200.0201.0000.9840.984
as_in0.0920.1010.0962.8533.3443.3950.0150.0070.0073.3374.5704.4980.0180.0090.0101.0000.9770.977
ast_es0.0530.0600.0563.1643.3133.3220.0160.0120.0103.9964.5434.4650.0340.0090.0111.0000.9850.987
az_az0.0540.0620.0572.8383.4553.4840.0140.0070.0052.9344.4954.4690.0280.0110.0111.0000.9810.980
be_by0.0370.0390.0382.9233.3623.3630.0190.0080.0083.2374.4744.4030.0240.0110.0091.0000.9820.984
bg_bg0.0410.0440.0433.1633.3263.3840.0150.0140.0103.6244.5414.4770.0230.0140.0151.0000.9820.983
bn_in0.0740.0790.0772.8263.3913.4410.0320.0060.0053.0124.5174.5450.0420.0120.0091.0000.9700.968
bs_ba0.0350.0420.0383.0903.3073.3670.0200.0090.0083.6464.4954.4530.0380.0130.0131.0000.9870.985
ca_es0.0310.0320.0323.1123.4063.4100.0150.0070.0073.3234.6124.5440.0220.0100.0101.0000.9800.983
ceb_ph0.0420.0490.0442.9313.2913.3600.0330.0100.0092.9314.3234.2120.0430.0230.0301.0000.9770.978
ckb_iq0.0830.0900.0882.8593.3923.4590.0290.0080.0063.1764.5574.4800.0310.0090.0111.0000.9780.973
cmn_hans_cn0.2660.2870.2803.1123.3973.3740.0130.0070.0063.5954.3734.3640.0260.0150.0121.0000.9840.984
cs_cz0.0310.0390.0343.0823.1663.1890.0150.0140.0143.5364.3204.1550.0300.0170.0221.0000.9830.982
cy_gb0.0900.0980.0953.1923.4463.4640.0180.0050.0053.8544.4714.4380.0310.0120.0071.0000.9840.981
da_dk0.0700.0820.0773.2723.4153.4250.0090.0070.0063.8924.5764.5780.0170.0090.0061.0000.9850.985
de_de0.0270.0300.0293.0043.2733.3260.0160.0090.0093.3714.3734.4150.0320.0140.0121.0000.9850.986
el_gr0.0530.0610.0573.0103.4073.4620.0200.0090.0073.2534.5524.3950.0440.0140.0161.0000.9820.983
en_us0.0510.0610.0612.9253.3933.4660.0530.0130.0103.0914.4594.4910.1040.0150.0161.0000.9820.976
es_4190.0210.0220.0223.1333.3363.3990.0150.0070.0063.6384.5464.5150.0300.0100.0121.0000.9780.978
et_ee0.0250.0270.0252.8163.3433.4000.0170.0080.0062.9684.5044.4130.0270.0120.0121.0000.9820.985
fa_ir0.0460.0530.0502.8613.4193.4570.0240.0080.0073.1844.5524.5170.0410.0090.0111.0000.9840.984
ff_sn0.1060.1210.1242.7653.3783.4080.0390.0150.0132.9164.4054.3720.0510.0220.0221.0000.9760.978
fi_fi0.0240.0270.0263.0433.2743.2770.0250.0110.0123.6094.3694.2490.0370.0250.0271.0000.9820.984
fr_fr0.0430.0520.0483.1253.2783.3360.0180.0140.0113.6334.4284.3860.0290.0150.0131.0000.9860.987
ga_ie0.2570.2660.2643.2733.3983.4480.0120.0130.0124.1574.4914.4880.0170.0100.0081.0000.9860.982
gl_es0.0290.0320.0302.7543.2563.3610.0390.0100.0083.3184.5424.4810.0300.0130.0161.0000.9830.983
gu_in0.0640.0720.0723.0363.3883.4280.0110.0060.0063.1434.5844.5610.0230.0120.0111.0000.9790.976
ha_ng0.0660.0720.0713.0263.2953.3200.0190.0120.0143.5124.3424.3130.0530.0180.0141.0000.9820.983
he_il0.1680.1790.1722.8013.2983.3850.0410.0130.0103.6254.5644.5970.0640.0120.0121.0000.9800.980
hi_in0.0640.0730.0692.7923.3093.3830.0410.0150.0113.2194.5364.5380.0540.0200.0181.0000.9760.974
hr_hr0.0450.0480.0482.7903.4093.4250.0430.0120.0113.3754.6014.4790.0280.0140.0211.0000.9710.969
hu_hu0.0430.0540.0493.0083.4253.4650.0130.0070.0053.1104.5114.3410.0350.0110.0181.0000.9800.978
hy_am0.0390.0430.0412.8313.4553.4800.0230.0060.0053.1184.5744.5280.0390.0080.0101.0000.9800.982
id_id0.0310.0460.0362.7573.3773.4130.0290.0090.0092.7274.4054.2200.0530.0190.0241.0000.9710.968
ig_ng0.1230.1350.1292.9533.3103.3790.0220.0080.0083.1844.3174.2240.0210.0190.0241.0000.9840.982
is_is0.0740.0840.0763.0853.3783.4150.0390.0250.0193.4524.4804.5070.0780.0350.0341.0000.9790.980
it_it0.0160.0170.0183.3253.4103.4320.0080.0080.0073.9444.5204.4540.0290.0140.0131.0000.9870.987
ja_jp0.2120.2250.2132.6733.4793.4530.0450.0060.0062.9134.5624.4720.0420.0110.0091.0000.9590.958
jv_id0.0470.0570.0532.9653.3893.4290.0180.0090.0072.9874.4134.3290.0420.0150.0191.0000.9730.977
ka_ge0.0500.0600.0552.7973.3803.4050.0200.0080.0073.1004.6524.6030.0400.0100.0101.0000.9840.982
kam_ke0.1160.1210.1192.8283.4023.4310.0180.0100.0083.1134.5304.4120.0340.0150.0131.0000.9780.977
kea_cv0.0430.0460.0442.8913.3103.3590.0180.0090.0083.0594.4634.4680.0390.0100.0101.0000.9840.984
kk_kz0.0340.0370.0372.7863.3443.3860.0170.0090.0083.1624.4804.3850.0250.0130.0161.0000.9860.984
km_kh0.1610.1820.1692.6223.4393.4280.0450.0090.0082.9864.4794.3480.0490.0130.0171.0000.9750.980
kn_in0.0510.0550.0522.6393.3573.4060.0330.0100.0072.8974.4414.4800.0350.0140.0121.0000.9720.968
ko_kr0.1730.1830.1792.9143.4313.4650.0360.0100.0093.1914.5074.4800.0300.0160.0161.0000.9790.980
ky_kg0.0430.0470.0472.5843.2723.3320.0270.0120.0103.0854.5124.4320.0430.0130.0141.0000.9860.984
lb_lu0.0870.1000.0963.2033.4523.4770.0150.0070.0063.8164.4754.4740.0260.0110.0101.0000.9870.986
lg_ug0.0800.0820.0843.2103.3543.3810.0130.0080.0083.8114.3354.2230.0300.0160.0171.0000.9830.985
ln_cd0.0440.0500.0472.3793.1863.2480.0540.0200.0152.6134.3644.2760.0350.0200.0211.0000.9710.967
lo_la0.2230.2520.2372.8903.4033.4840.0360.0110.0082.9254.2994.3720.0480.0230.0181.0000.9750.974
lt_lt0.0400.0440.0422.7663.2873.3690.0240.0090.0073.4354.5384.5260.0290.0120.0111.0000.9820.987
luo_ke0.0550.0620.0592.8773.3713.4380.0280.0120.0092.7524.5354.4270.0400.0180.0241.0000.9880.985
lv_lv0.0280.0310.0293.1443.3753.4130.0180.0090.0063.5644.5174.4950.0300.0110.0121.0000.9840.982
mi_nz0.0770.1120.0872.9823.3523.3720.0220.0110.0113.0444.2854.3180.0520.0180.0201.0000.9820.984
mk_mk0.0200.0210.0202.9543.4263.4680.0300.0070.0063.3634.5614.5110.0360.0130.0151.0000.9810.980
ml_in0.0510.0550.0532.7353.3483.3900.0280.0060.0063.0814.4594.4350.0220.0120.0111.0000.9660.972
mn_mn0.0870.1050.0953.0453.3953.4370.0190.0080.0063.2044.3894.3580.0240.0120.0121.0000.9820.980
mr_in0.0760.0850.0822.8733.3433.4060.0160.0060.0053.0134.4934.3860.0210.0110.0171.0000.9770.977
ms_my0.0990.1180.1042.6123.1693.2350.0230.0270.0222.6704.4254.3000.0260.0220.0221.0000.9710.972
mt_mt0.0350.0370.0363.0483.3723.4570.0180.0090.0063.4014.5914.5320.0370.0100.0131.0000.9740.974
my_mm0.1510.1760.1653.0233.3513.3590.0190.0070.0063.3954.4414.4840.0420.0140.0091.0000.9750.979
nb_no0.0510.0540.0523.2263.3363.4170.0150.0130.0103.8574.5574.4680.0260.0110.0131.0000.9660.977
ne_np0.0880.1560.1062.9033.3153.2920.0330.0090.0113.1854.4704.2530.0800.0180.0361.0000.9750.975
nl_nl0.0340.0390.0412.7373.4463.4770.0200.0090.0082.7754.5794.5720.0280.0170.0141.0000.9850.986
nso_za0.0730.0840.0762.9073.3423.3780.0260.0100.0083.5414.4984.4500.0430.0110.0111.0000.9850.988
ny_mw0.0660.0770.0692.3113.3483.3630.0530.0100.0092.6144.4244.2510.0330.0120.0161.0000.9640.969
oc_fr0.0980.1130.1022.3453.3303.3800.0380.0070.0062.4624.4124.2020.0220.0130.0171.0000.9640.958
om_et0.1720.1790.1803.1763.3233.3790.0650.0370.0473.6884.2794.2280.1330.0880.1251.0000.9850.985
pa_in0.0760.1100.0812.5553.2773.3720.0460.0160.0132.7374.4084.4610.0540.0260.0161.0000.9690.963
pl_pl0.0300.0360.0332.9123.2743.3330.0230.0130.0113.5444.6214.5730.0440.0090.0091.0000.9840.983
ps_af0.1610.1700.1672.6823.2673.3060.0330.0130.0133.1654.5544.5880.0400.0170.0161.0000.9810.980
pt_br0.0280.0320.0382.9513.3993.4500.0220.0070.0063.3644.4814.5140.0370.0100.0091.0000.9690.964
ro_ro0.0350.0460.0482.7473.4203.4800.0270.0080.0062.8694.5244.4860.0310.0100.0111.0000.9760.974
ru_ru0.0420.0460.0433.0053.3073.3640.0180.0120.0093.4204.5504.4900.0410.0130.0131.0000.9840.986
sd_in0.0840.0920.0902.9413.3013.3630.0230.0100.0083.2134.4834.4310.0370.0120.0121.0000.9730.971
sk_sk0.0220.0250.0233.1113.3463.4000.0200.0110.0103.5304.5284.4450.0380.0140.0181.0000.9830.983
sl_si0.0460.0510.0462.7383.2873.3710.0220.0130.0103.0914.5714.4950.0400.0120.0131.0000.9830.986
sn_zw0.0420.0540.0462.9773.3993.4340.0180.0090.0082.4014.4244.3270.0190.0120.0131.0000.9660.968
so_so0.1330.1450.1392.5533.2623.3090.0350.0120.0123.0304.4394.4140.0310.0170.0131.0000.9770.977
sr_rs0.8950.8950.8942.9223.4533.4900.0430.0070.0063.4774.5834.5730.0480.0080.0081.0000.9810.979
sv_se0.0560.0610.0593.0483.3803.4480.0150.0090.0073.3244.5764.5640.0300.0080.0081.0000.9790.980
sw_ke0.0330.0350.0332.9563.3423.3620.0250.0130.0123.1204.3954.2300.0680.0180.0261.0000.9810.977
ta_in0.0970.1050.1002.6773.2643.3170.0310.0150.0122.9534.3494.2990.0450.0250.0281.0000.9700.969
te_in0.0910.1030.0962.6893.3223.3500.0490.0130.0103.2604.4324.3300.0570.0240.0261.0000.9720.969
tg_tj0.0450.0470.0462.8213.4143.4500.0250.0080.0073.1994.6204.5890.0260.0110.0121.0000.9850.985
th_th0.1060.1160.1112.8213.4993.5120.0210.0050.0052.7924.3234.2910.0210.0150.0141.0000.9790.979
tr_tr0.0400.0440.0413.0693.4173.4490.0200.0080.0073.3654.5564.5240.0390.0090.0091.0000.9730.973
uk_ua0.0390.0460.0423.1803.4263.4710.0220.0080.0073.7944.5314.5260.0470.0130.0141.0000.9870.985
umb_ao0.0830.0970.0982.3883.3233.3460.0350.0210.0212.3994.4694.1930.0470.0280.0461.0000.9690.964
ur_pk0.0990.1060.1032.7853.3283.3800.0420.0170.0132.9184.6504.4970.0340.0120.0171.0000.9800.981
uz_uz0.0600.0730.0632.8243.1763.2290.0360.0160.0153.0334.1884.2700.0270.0200.0151.0000.9870.986
vi_vn0.1190.1460.1412.8053.3303.3530.0220.0110.0102.8784.2404.1690.0330.0180.0161.0000.9750.977
wo_sn0.1110.1350.1192.4963.1533.2470.0650.0510.0322.4304.1324.1240.0880.0550.0451.0000.9770.975
xh_za0.0690.0950.0892.2203.1723.3090.0380.0340.0232.5704.3924.4040.0220.0300.0211.0000.9770.977
yo_ng0.1790.1930.1843.0803.2803.3330.0200.0110.0113.2774.2494.1790.0370.0170.0251.0000.9790.983
yue_hant_hk0.2530.2730.2602.9683.3703.3780.0330.0130.0143.4554.4654.3880.0460.0160.0151.0000.9840.986
zu_za0.0650.0790.0723.0333.3153.3580.0130.0110.0083.2404.3594.1850.0240.0140.0171.0000.9840.983
+
+

Note: Values loaded directly from the evaluation export.

+
+ +
+
+ + + + + diff --git a/index.html b/index.html index 832feef610c1bee82235cf41db76fb9a1fe40a05..2eb176c5bae8479d73d66c7b6dfc4d8ebd20ebe8 100644 --- a/index.html +++ b/index.html @@ -35,7 +35,7 @@

Sidon: Fast and Robust Open-Source Multilingual Speech Restoration - for Dataset Restoration

+ for Dataset Cleansing
Wataru Nakata, Yuki Saito, @@ -58,12 +58,21 @@ - - Code & Demos + Code + + + + + + 🤗 + + Live Demo
@@ -89,21 +98,19 @@

Abstract

- Large-scale text-to-speech (TTS) systems are limited by the scarcity of clean, multilingual recordings. We - introduce Sidon, a fast, open-source speech restoration model that converts noisy in-the-wild - speech into studio-quality speech and scales to dozens of languages. -

-

- Sidon consists of two models: a w2v-BERT 2.0 finetuned feature predictor that cleanses features from noisy - speech and a vocoder trained to synthesize restored speech from the cleansed features. Sidon achieves - restoration performance comparable to Miipher (Google's internal speech restoration model) for dataset - restoration in speech synthesis. -

-

- Sidon is computationally efficient, running up to 7,279× faster than real time on a single GPU. We further - show that training a TTS model using ASR corpora cleansed with Sidon improves the naturalness of synthetic - speech in a zero-shot setting. Code and models will be released to facilitate reproducible dataset - restoration for the research community. + Large-scale text-to-speech (TTS) systems are limited by the scarcity of clean, + multilingual recordings. We introduce Sidon, a fast, open-source + speech restoration model that converts noisy in-the-wild speech into + studio-quality speech and scales to dozens of languages. Sidon consists of + two models: w2v-BERT 2.0 finetuned feature predictor to cleanse features from noisy speech + and vocoder trained to synthesize restored speech from the cleansed features. + Sidon achieves restoration performance comparable to Miipher: Google's internal speech restoration model with + the aim of dataset cleansing for speech synthesis. Sidon is also computationally + efficient, running up to 3,390× faster than real time on a + single GPU. We further show that training a TTS model using a Sidon-cleansed automatic speech + recognition corpus improves the quality of synthetic + speech in a zero-shot setting. Code and model are released to + facilitate reproducible dataset cleansing for the research community.

@@ -116,8 +123,32 @@
+

Full Multilingual Results (FLEURS)

+
+

The full multilingual evaluation table is large. It is hidden by default to keep the page lightweight.

+
+
+ Show results table +

Loads an embedded page with all language-wise metrics.

+
+ +
+
+
+
+ + +
+
+ +

Multilingual Samples

+
- +

English Demo Samples

+
@@ -170,4 +201,4 @@ - \ No newline at end of file + diff --git a/main.py b/main.py new file mode 100644 index 0000000000000000000000000000000000000000..1cb650e68d00a9d0f532d9d01c665dbc7ff25902 --- /dev/null +++ b/main.py @@ -0,0 +1,6 @@ +def main(): + print("Hello from sidonsamples!") + + +if __name__ == "__main__": + main() diff --git a/multilingual_demo/cmn_hans_cn/miipher/9164262831491631784.wav b/multilingual_demo/cmn_hans_cn/miipher/9164262831491631784.wav new file mode 100644 index 0000000000000000000000000000000000000000..a597fe6ea1b40a8d7b92e8d6995a87d8d4b60276 --- /dev/null +++ b/multilingual_demo/cmn_hans_cn/miipher/9164262831491631784.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:61577e8f1375fa66bb6c40a9d4f111e150886fc28e01ac61bfb62ee33ac269d9 +size 560718 diff --git a/multilingual_demo/cmn_hans_cn/noisy/9164262831491631784.wav b/multilingual_demo/cmn_hans_cn/noisy/9164262831491631784.wav new file mode 100644 index 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0000000000000000000000000000000000000000..b6a82bc540d188addd3374f00f3badcc59231d75 --- /dev/null +++ b/multilingual_demo/tr_tr/noisy/1769159075613236297.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5d776026f15946d1b7413d130037180552f9e218dcaa34417caf65ba366bb07 +size 284238 diff --git a/multilingual_demo/tr_tr/sidon/1769159075613236297.wav b/multilingual_demo/tr_tr/sidon/1769159075613236297.wav new file mode 100644 index 0000000000000000000000000000000000000000..82713f5361131418943deab30fa858014185058e --- /dev/null +++ b/multilingual_demo/tr_tr/sidon/1769159075613236297.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:18a9a4b47acd36daa5465ccd322c010d7e9dbee492410a642573d65c236d5a9d +size 840746 diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..e23f3aa137f4958c2908f86462d45cb2db6b5f65 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,12 @@ +[project] +name = "sidonsamples" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "librosa>=0.11.0", + "matplotlib>=3.10.6", + "numpy>=2.3.3", + "soundfile>=0.13.1", +] diff --git a/static/css/index.css b/static/css/index.css index 21076ef552588e5831c9e503067762142cb7c9c0..38ad92c7676c59779ca13dca00834d03f4fff491 100644 --- a/static/css/index.css +++ b/static/css/index.css @@ -155,3 +155,26 @@ body { #interpolation-image-wrapper img { border-radius: 5px; } + +/* Samples section */ +.sample-card { + margin-bottom: 1.5rem; +} + +.spectrogram-pair { + margin-top: 0.75rem; + border: 1px solid #e5e5e5; + border-radius: 6px; + background: #fafafa; + padding: 6px; +} + +.spectrogram { + width: 100%; + height: auto; + display: block; +} + +audio.is-fullwidth { + width: 100%; +} diff --git a/static/js/index.js b/static/js/index.js index 8daed0c67f71a02472f1c35c474aa0888974a6ec..d0d2db82a83b69f121c20105a0eae167b614b194 100644 --- a/static/js/index.js +++ b/static/js/index.js @@ -75,4 +75,80 @@ $(document).ready(function() { bulmaSlider.attach(); + // Lazy-load the large FLEURS results iframe when the details is opened + const fleurs = document.getElementById('fleurs-details'); + if (fleurs) { + fleurs.addEventListener('toggle', function() { + if (this.open) { + const iframe = this.querySelector('iframe[data-src]'); + if (iframe && !iframe.src) { + iframe.src = iframe.dataset.src; + } + } + }); + } + }) + +// Render audio samples from assets/samples.json +async function renderSamples() { + const rootMulti = document.getElementById('samples-multilingual-root'); + const rootEn = document.getElementById('samples-english-root'); + if (!rootMulti && !rootEn) return; + try { + const res = await fetch('assets/samples.json', { cache: 'no-cache' }); + if (!res.ok) throw new Error('Failed to load samples.json'); + const data = await res.json(); + if (!Array.isArray(data.samples)) throw new Error('Invalid samples.json'); + + if (rootMulti) rootMulti.innerHTML = ''; + if (rootEn) rootEn.innerHTML = ''; + + for (const s of data.samples) { + const isEnglish = [s.noisy, s.miipher, s.restored, s.sidon, s.gt] + .filter(Boolean) + .some(p => typeof p === 'string' && p.startsWith('english_demo/')); + const container = isEnglish ? rootEn : rootMulti; + if (!container) continue; + const title = s.title || s.id || 'Sample'; + const cols = []; + if (isEnglish) { + if (s.gt) cols.push({ label: 'Noisy', src: s.gt, mel: s.gt_mel }); + if (s.miipher) cols.push({ label: 'Miipher', src: s.miipher, mel: s.miipher_mel }); + if (s.restored || s.sidon) cols.push({ label: 'Sidon', src: s.restored || s.sidon, mel: s.restored_mel || s.sidon_mel }); + } else { + if (s.noisy) cols.push({ label: 'Noisy', src: s.noisy, mel: s.noisy_mel }); + if (s.miipher) cols.push({ label: 'Miipher-2', src: s.miipher, mel: s.miipher_mel }); + if (s.restored || s.sidon) cols.push({ label: 'Sidon Restored', src: s.restored || s.sidon, mel: s.restored_mel || s.sidon_mel }); + if (s.gt) cols.push({ label: 'Ground Truth', src: s.gt, mel: s.gt_mel }); + } + + if (cols.length === 0) continue; + + const card = document.createElement('div'); + card.className = 'box sample-card'; + const colsHtml = cols.map(c => ` +
+
${c.label}
+ + ${c.mel ? `
${c.label} mel-spectrogram
` : ''} +
`).join(''); + + card.innerHTML = ` +

${title}

+
${colsHtml} +
`; + + container.appendChild(card); + } + } catch (e) { + if (rootMulti) rootMulti.innerHTML = `
Could not load samples: ${e.message}. Ensure assets/samples.json exists.
`; + if (rootEn) rootEn.innerHTML = ''; + } +} + +// Kick off rendering after DOM and scripts are ready +document.addEventListener('DOMContentLoaded', renderSamples); diff --git a/tools/__pycache__/make_mel_images.cpython-313.pyc b/tools/__pycache__/make_mel_images.cpython-313.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f0949addb1a550d4da9600134613389fc2ef98bc Binary files /dev/null and b/tools/__pycache__/make_mel_images.cpython-313.pyc differ diff --git a/tools/make_mel_images.py b/tools/make_mel_images.py new file mode 100644 index 0000000000000000000000000000000000000000..05bf6309012fc36b7eae29337b256f05c4617fb4 --- /dev/null +++ b/tools/make_mel_images.py @@ -0,0 +1,62 @@ +#!/usr/bin/env python3 +import argparse +from pathlib import Path + +import numpy as np +import librosa +import librosa.display +import matplotlib.pyplot as plt + + +def save_mel_image( + audio_path: Path, + out_path: Path, + sr: int = 22050, + n_fft: int = 1024, + hop_length: int = 256, + n_mels: int = 80, + fmin: int = 0, + fmax: int | None = 8000, +): + y, _ = librosa.load(str(audio_path), sr=sr, mono=True) + S = librosa.feature.melspectrogram( + y=y, sr=sr, n_fft=n_fft, hop_length=hop_length, n_mels=n_mels, fmin=fmin, fmax=fmax + ) + S_db = librosa.power_to_db(S, ref=np.max) + + plt.figure(figsize=(8, 3), dpi=150) + librosa.display.specshow(S_db, sr=sr, hop_length=hop_length, x_axis=None, y_axis=None, cmap="magma") + plt.axis("off") + plt.tight_layout(pad=0) + out_path.parent.mkdir(parents=True, exist_ok=True) + plt.savefig(out_path, bbox_inches="tight", pad_inches=0) + plt.close() + + +def main(): + p = argparse.ArgumentParser(description="Generate mel-spectrogram image from an audio file.") + p.add_argument("audio", type=Path, help="Path to input audio (wav/flac/mp3)") + p.add_argument("output", type=Path, help="Path to output image (png/jpg)") + p.add_argument("--sr", type=int, default=22050) + p.add_argument("--n_fft", type=int, default=1024) + p.add_argument("--hop", dest="hop_length", type=int, default=256) + p.add_argument("--mels", dest="n_mels", type=int, default=80) + p.add_argument("--fmin", type=int, default=0) + p.add_argument("--fmax", type=int, default=8000) + args = p.parse_args() + + save_mel_image( + audio_path=args.audio, + out_path=args.output, + sr=args.sr, + n_fft=args.n_fft, + hop_length=args.hop_length, + n_mels=args.n_mels, + fmin=args.fmin, + fmax=args.fmax, + ) + + +if __name__ == "__main__": + main() + diff --git a/tools/make_mels_from_manifest.py b/tools/make_mels_from_manifest.py new file mode 100644 index 0000000000000000000000000000000000000000..17632c5298c00cd02951b2b623eb1e19fea08714 --- /dev/null +++ b/tools/make_mels_from_manifest.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +import argparse +import json +from pathlib import Path +from typing import Dict, Any + +# Reuse the single-file generator +import sys +sys.path.append(str(Path(__file__).parent)) +from make_mel_images import save_mel_image # type: ignore + + +SUPPORTED_KEYS = [ + ("noisy", "noisy_mel"), + ("miipher", "miipher_mel"), + ("restored", "restored_mel"), + ("sidon", "sidon_mel"), + ("gt", "gt_mel"), +] + + +def process_manifest( + manifest_path: Path, + out_dir: Path, + inplace: bool = True, + sr: int = 22050, + n_fft: int = 1024, + hop_length: int = 256, + n_mels: int = 80, + fmin: int = 0, + fmax: int | None = 8000, +) -> Path: + data = json.loads(manifest_path.read_text()) + samples = data.get("samples", []) + out_dir.mkdir(parents=True, exist_ok=True) + + for s in samples: + sample_id = s.get("id") or s.get("title", "sample").lower().replace(" ", "_") + for key, mel_key in SUPPORTED_KEYS: + audio_path = s.get(key) + if not audio_path: + continue + audio_p = Path(audio_path) + if not audio_p.exists(): + print(f"[warn] missing audio: {audio_path}") + continue + out_path = out_dir / f"{sample_id}_{key}.png" + print(f"[mel] {audio_path} -> {out_path}") + save_mel_image( + audio_path=audio_p, + out_path=out_path, + sr=sr, + n_fft=n_fft, + hop_length=hop_length, + n_mels=n_mels, + fmin=fmin, + fmax=fmax, + ) + s[mel_key] = str(out_path) + + out_path = manifest_path if inplace else manifest_path.with_suffix(".with_mels.json") + out_path.write_text(json.dumps(data, ensure_ascii=False, indent=2) + "\n") + return out_path + + +def main(): + p = argparse.ArgumentParser(description="Generate mel images for all samples in a manifest and update it.") + p.add_argument("manifest", type=Path, nargs="?", default=Path("assets/samples.json")) + p.add_argument("--out_dir", type=Path, default=Path("assets/mels")) + p.add_argument("--no_inplace", action="store_true", help="Write to .with_mels.json instead of overwriting") + p.add_argument("--sr", type=int, default=22050) + p.add_argument("--n_fft", type=int, default=1024) + p.add_argument("--hop", dest="hop_length", type=int, default=256) + p.add_argument("--mels", dest="n_mels", type=int, default=80) + p.add_argument("--fmin", type=int, 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