added data and test.py

.gitignore

@@ -0,0 +1 @@
+__pycache__

README.md

@@ -18,6 +18,8 @@ extra_gated_prompt: "When using this dataset to download LibriTTS, you agree to
 
 > This dataset is identical to **[cdminix/libritts-aligned](https://huggingface.co/datasets/cdminix/libritts-aligned)** except it uses the newly released LibriTTS-R corpus. Please cite **[Y. Koizumi, et al., "LibriTTS-R: Restoration of a Large-Scale Multi-Speaker TTS Corpus", Interspeech 2023](https://google.github.io/df-conformer/librittsr/)**
 
+*When using this dataset to download LibriTTS-R, make sure you agree to the terms on https://www.openslr.org*
+
 # Dataset Card for LibriTTS-R with Forced Alignments (and Measures)
 
 This dataset downloads LibriTTS-R and preprocesses it on your machine to create alignments using [montreal forced aligner](https://montreal-forced-aligner.readthedocs.io/en/latest/).
@@ -28,7 +30,7 @@ When running this the first time, it can take an hour or two, but subsequent run
 - ``pip install alignments phones`` **(required)**
 - ``pip install speech-collator`` (optional)
 
-*Note: version >=0.0.14 of alignments is required for this corpus*
+*Note: version >=0.0.15 of alignments is required for this corpus*
 
 ## Example Item
 

compute_stats.py

@@ -1,3 +0,0 @@
-from datasets import load_dataset
-
-dataset = load_dataset("libritts-r-aligned.py")

data/phone2idx.json

@@ -1 +1 @@
-{"<pad>": 0, "<mask>": 1, "<unk>": 2, "[SILENCE]": 3, "e\u026a": 4, "v": 5, "\u025b": 6, "\u0279": 7, "i": 8, "\u026a": 9, "m": 10, "p": 11, "\u0254": 12, "t": 13, "\u028c": 14, "n": 15, "\u0254\u026a": 16, "[COMMA]": 17, "\u00e6": 18, "d": 19, "z": 20, "b": 21, "l": 22, "k": 23, "u": 24, "\u0292": 25, "a\u026a": 26, "f": 27, "s": 28, "w": 29, "[FULL STOP]": 30, "j": 31, "o\u028a": 32, "\u0261": 33, "\u00f0": 34, "\u0283": 35, "\u014b": 36, "\u025c\u02de": 37, "h": 38, "\u0251": 39, "\u02cc\u026a": 40, "\u03b8": 41, "d\u0292": 42, "t\u0283": 43, "\u02cci": 44, "\u02cco\u028a": 45, "a\u028a": 46, "\u02cc\u025c\u02de": 47, "\u02cc\u028a": 48, "\u028a": 49, "[EXCLAMATION MARK]": 50, "\u02ccu": 51, "\u02cca\u026a": 52, "\u02cc\u025b": 53, "[QUESTION MARK]": 54, "\u02cc\u028c": 55, "[SEMICOLON]": 56, "\u02cc\u0254": 57, "\u02cce\u026a": 58, "\u02cc\u00e6": 59, "\u02cca\u028a": 60, "\u02cc\u0251": 61, "\u02cc\u0254\u026a": 62}
+{"[SILENCE]": 0, "\u00f0": 1, "\u026a": 2, "s": 3, "w": 4, "\u028c": 5, "z": 6, "n": 7, "\u0251": 8, "t": 9, "[COMMA]": 10, "\u00e6": 11, "m": 12, "e\u026a": 13, "i": 14, "\u0279": 15, "l": 16, "\u03b8": 17, "\u014b": 18, "d": 19, "k": 20, "\u02cc\u00e6": 21, "[FULL STOP]": 22, "h": 23, "b": 24, "p": 25, "f": 26, "\u025b": 27, "\u0283": 28, "v": 29, "\u025c\u02de": 30, "a\u026a": 31, "\u0292": 32, "[SEMICOLON]": 33, "o\u028a": 34, "d\u0292": 35, "\u028a": 36, "\u02cce\u026a": 37, "a\u028a": 38, "\u0254": 39, "u": 40, "\u02cc\u025b": 41, "j": 42, "\u0261": 43, "\u02cc\u026a": 44, "\u0254\u026a": 45, "t\u0283": 46, "[EXCLAMATION MARK]": 47, "\u02cca\u026a": 48, "\u02cc\u025c\u02de": 49, "\u02cc\u028a": 50, "[QUESTION MARK]": 51, "\u02cco\u028a": 52, "\u02cc\u0251": 53, "\u02cci": 54, "\u02cca\u028a": 55, "\u02cc\u0254": 56, "\u02cc\u028c": 57, "\u02ccu": 58, "\u02cc\u0254\u026a": 59, "<pad>": 60, "<unk>": 61, "<mask>": 62}

data/speaker2idx.json

@@ -1 +1 @@
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1886, "3394": 1887, "8058": 1888, "6794": 1889, "4205": 1890, "7480": 1891, "3020": 1892, "1545": 1893, "7679": 1894, "8631": 1895, "614": 1896, "8897": 1897, "6131": 1898, "1784": 1899, "846": 1900, "6758": 1901, "283": 1902, "4659": 1903, "613": 1904, "3488": 1905, "421": 1906, "277": 1907, "7683": 1908, "884": 1909, "1746": 1910, "4234": 1911, "1110": 1912, "3647": 1913, "428": 1914, "2553": 1915, "6138": 1916, "8432": 1917, "6590": 1918, "7026": 1919, "6332": 1920, "622": 1921, "5118": 1922, "444": 1923, "8005": 1924, "7871": 1925, "2967": 1926, "5970": 1927, "2356": 1928, "5725": 1929, "8208": 1930, "6950": 1931, "5983": 1932, "5977": 1933, "215": 1934, "8466": 1935, "5379": 1936, "3045": 1937, "6705": 1938, "6153": 1939, "2735": 1940, "2351": 1941, "6368": 1942, "8808": 1943, "1985": 1944, "3417": 1945, "6733": 1946, "4090": 1947, "4894": 1948, "8033": 1949, "6539": 1950, "6196": 1951, "5979": 1952, "2152": 1953, "4063": 1954, "6506": 1955, "3848": 1956, "1347": 1957, "4407": 1958, "5784": 1959, "3641": 1960, "6333": 1961, "1572": 1962, "876": 1963, "7215": 1964, "8296": 1965, "6902": 1966, "8262": 1967, "5545": 1968, "2552": 1969, "4235": 1970, "4463": 1971, "6106": 1972, "7649": 1973, "7823": 1974, "3679": 1975, "8291": 1976, "5325": 1977, "2100": 1978, "6792": 1979, "7640": 1980, "4693": 1981, "4455": 1982, "4667": 1983, "1924": 1984, "1544": 1985, "3021": 1986, "4660": 1987, "7223": 1988, "4009": 1989, "5913": 1990, "1977": 1991, "2198": 1992, "8664": 1993, "8200": 1994, "6735": 1995, "6351": 1996, "3411": 1997, "7277": 1998, "7879": 1999, "2361": 2000, "474": 2001, "3885": 2002, "3871": 2003, "6967": 2004, "1187": 2005, "679": 2006, "1341": 2007, "6356": 2008, "6199": 2009, "684": 2010, "6951": 2011, "7883": 2012, "7848": 2013, "7246": 2014, "5949": 2015, "4295": 2016, "5181": 2017, "489": 2018, "6358": 2019, "3088": 2020, "1370": 2021, "1714": 2022, "3271": 2023, "5340": 2024, "3285": 2025, "5172": 2026, "2733": 2027, "1384": 2028, "7073": 2029, "1736": 2030, "6513": 2031, "6177": 2032, "7263": 2033, "7607": 2034, "6974": 2035, "3896": 2036, "807": 2037, "2140": 2038, "4085": 2039, "6184": 2040, "4415": 2041, "2988": 2042, "6726": 2043, "2943": 2044, "8445": 2045, "4872": 2046, "4423": 2047, "3606": 2048, "7609": 2049, "2975": 2050, "6945": 2051, "7299": 2052, "690": 2053, "4078": 2054, "3433": 2055, "2185": 2056, "238": 2057, "7255": 2058, "1559": 2059, "1901": 2060, "8044": 2061, "5569": 2062, "253": 2063, "3467": 2064, "7691": 2065, "6743": 2066, "1566": 2067, "2919": 2068, "5132": 2069, "608": 2070, "4484": 2071, "2541": 2072, "7239": 2073, "402": 2074, "6549": 2075, "1595": 2076, "2122": 2077, "7654": 2078, "4687": 2079, "5567": 2080, "6540": 2081, "7492": 2082, "4217": 2083, "1765": 2084, "3238": 2085, "4821": 2086, "265": 2087, "8072": 2088, "8240": 2089, "7208": 2090, "6311": 2091, "7839": 2092, "5355": 2093, "3290": 2094, "8678": 2095, "7092": 2096, "4273": 2097, "6943": 2098, "1168": 2099, "4422": 2100, "4280": 2101, "6178": 2102, "2148": 2103, "7608": 2104, "1166": 2105, "7433": 2106, "5700": 2107, "7265": 2108, "8444": 2109, "2522": 2110, "2374": 2111, "3060": 2112, "6512": 2113, "2380": 2114, "1505": 2115, "1161": 2116, "1353": 2117, "7898": 2118, "5952": 2119, "8671": 2120, "8443": 2121, "466": 2122, "8215": 2123, "1708": 2124, "264": 2125, "8625": 2126, "5906": 2127, "432": 2128, "8087": 2129, "7699": 2130, "4211": 2131, "4447": 2132, "1569": 2133, "1931": 2134, "8246": 2135, "263": 2136, "1132": 2137, "3665": 2138, "3033": 2139, "4216": 2140, "6773": 2141, "7467": 2142, "3691": 2143, "6548": 2144, "255": 2145, "8042": 2146, "7238": 2147, "3698": 2148, "1938": 2149, "5796": 2150, "6583": 2151, "404": 2152, "252": 2153, "4218": 2154, "1593": 2155, "5765": 2156, "8846": 2157, "1331": 2158, "5791": 2159, "4015": 2160, "5287": 2161, "6402": 2162, "46": 2163, "7988": 2164, "6254": 2165, "1085": 2166, "4193": 2167, "1618": 2168, "4964": 2169, "5628": 2170, "2051": 2171, "2607": 2172, "7147": 2173, "5280": 2174, "4352": 2175, "2297": 2176, "8302": 2177, "3148": 2178, "585": 2179, "1049": 2180, "2067": 2181, "4156": 2182, "3373": 2183, "77": 2184, "123": 2185, "8334": 2186, "4955": 2187, "5077": 2188, "927": 2189, "7512": 2190, "5245": 2191, "3179": 2192, "1844": 2193, "4738": 2194, "6853": 2195, "8565": 2196, "5874": 2197, "3911": 2198, "6807": 2199, "8367": 2200, "726": 2201, "8531": 2202, "3744": 2203, "6652": 2204, "5023": 2205, "177": 2206, "8536": 2207, "8394": 2208, "3318": 2209, "7125": 2210, "6003": 2211, "3571": 2212, "8199": 2213, "1828": 2214, "8356": 2215, "8164": 2216, "8500": 2217, "2208": 2218, "2237": 2219, "1680": 2220, "5220": 2221, "728": 2222, "6035": 2223, "3547": 2224, "1674": 2225, "8197": 2226, "4930": 2227, "7584": 2228, "1819": 2229, "5471": 2230, "7326": 2231, "4791": 2232, "4396": 2233, "5244": 2234, "5076": 2235, "3381": 2236, "5412": 2237, "7177": 2238, "85": 2239, "2405": 2240, "1280": 2241, "5620": 2242, "6051": 2243, "1274": 2244, "4706": 2245, "5082": 2246, "317": 2247, "8590": 2248, "4701": 2249, "7170": 2250, "82": 2251, "921": 2252, "7514": 2253, "7389": 2254, "780": 2255, "49": 2256, "5886": 2257, "310": 2258, "774": 2259, "789": 2260, "6636": 2261, "4161": 2262, "1621": 2263, "319": 2264, "2262": 2265, "7522": 2266, "2050": 2267, "2606": 2268, "6609": 2269, "584": 2270, "742": 2271, "1084": 2272, "5424": 2273, "4192": 2274, "1414": 2275, "7387": 2276, "47": 2277, "4991": 2278, "2854": 2279, "2068": 2280, "7942": 2281, "4965": 2282, "147": 2283, "3926": 2284, "4931": 2285, "7327": 2286, "2003": 2287, "3125": 2288, "7585": 2289, "3541": 2290, "4936": 2291, "1681": 2292, "5013": 2293, "3780": 2294, "7320": 2295, "3319": 2296, "6459": 2297, "5826": 2298, "720": 2299, "1643": 2300, "3928": 2301, "7786": 2302, "5217": 2303, "1485": 2304, "727": 2305, "4799": 2306, "8392": 2307, "4594": 2308, "25": 2309, "4104": 2310, "2412": 2311, "2078": 2312, "3752": 2313, "1988": 2314, "1919": 2315, "422": 2316, "3000": 2317, "1993": 2318, 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data/stats.json

@@ -0,0 +1 @@
+{"mean_pitch": 166.3789520263672, "std_pitch": 28.15303611755371, "mean_energy": 0.12094783782958984, "std_energy": 0.09327217191457748, "mean_duration": 1.417296846080225, "std_duration": 0.902642275968069}

libritts-r-aligned.py

@@ -8,7 +8,7 @@ import pickle
 import datasets
 import pandas as pd
 import numpy as np
-from alignments.datasets.libritts import LibrittsDataset
+from alignments.datasets.libritts import LibrittsRDataset
 from tqdm.contrib.concurrent import process_map
 from tqdm.auto import tqdm
 from multiprocessing import cpu_count
@@ -193,7 +193,7 @@ class LibriTTSAlign(datasets.GeneratorBasedBuilder):
                 url = None
             if os.path.exists(src_dir):
                 url = None
-            ds = LibrittsDataset(
+            ds = LibrittsRDataset(
                 target_directory=tgt_dir,
                 source_directory=src_dir,
                 source_url=url,

test.py

@@ -0,0 +1,209 @@
+from datasets import load_dataset
+from tqdm.auto import tqdm
+from speech_collator import SpeechCollator
+import json
+from torch.utils.data import DataLoader
+import torch
+from vocex import Vocex
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+
+vocex = Vocex.from_pretrained("cdminix/vocex")
+
+
+dataset = load_dataset("libritts-r-aligned.py")
+
+# Load the speaker2idx and phone2idx dictionaries
+with open("data/speaker2idx.json", "r") as f:
+    speaker2idx = json.load(f)
+    idx2speaker = {v: k for k, v in speaker2idx.items()}
+with open("data/phone2idx.json", "r") as f:
+    phone2idx = json.load(f)
+    idx2phone = {v: k for k, v in phone2idx.items()}
+
+collator = SpeechCollator(
+    speaker2idx=speaker2idx,
+    phone2idx=phone2idx,
+)
+
+dataloader = DataLoader(
+    dataset["dev"],
+    batch_size=1,
+    shuffle=False,
+    collate_fn=collator.collate_fn,
+    num_workers=4,
+)
+
+def resample(x, vpw=5):
+    return np.interp(np.linspace(0, 1, vpw), np.linspace(0, 1, len(x)), x)
+
+mean_pitchs = []
+std_pitchs = []
+mean_energys = []
+std_energys = []
+mean_durations = []
+std_durations = []
+
+for item in tqdm(dataloader):
+    result = vocex.model(item["mel"], inference=True)
+    pitch = result["measures"]["pitch"]
+    energy = result["measures"]["energy"]
+    va = result["measures"]["voice_activity_binary"]
+    mean_pitch = pitch.mean()
+    std_pitch = pitch.std()
+    mean_energy = energy.mean()
+    std_energy = energy.std()
+    durations = item["phone_durations"].squeeze().numpy()
+    durations = np.log(durations + 1)
+    mean_duration = durations.mean()
+    std_duration = durations.std()
+    mean_pitchs.append(mean_pitch)
+    std_pitchs.append(std_pitch)
+    mean_energys.append(mean_energy)
+    std_energys.append(std_energy)
+    mean_durations.append(mean_duration)
+    std_durations.append(std_duration)
+
+mean_pitch = np.mean(mean_pitchs)
+std_pitch = np.mean(std_pitchs)
+mean_energy = np.mean(mean_energys)
+std_energy = np.mean(std_energys)
+mean_duration = np.mean(mean_durations)
+std_duration = np.mean(std_durations)
+
+# save the stats
+stats = {
+    "mean_pitch": float(mean_pitch),
+    "std_pitch": float(std_pitch),
+    "mean_energy": float(mean_energy),
+    "std_energy": float(std_energy),
+    "mean_duration": float(mean_duration),
+    "std_duration": float(std_duration),
+}
+
+with open("data/stats.json", "w") as f:
+    json.dump(stats, f)
+
+for item in tqdm(dataloader):
+    plt.figure(figsize=(20, 10))
+    plt.subplot(4, 1, 1)
+    plt.title("Mel spectrogram")
+    plt.imshow(item["mel"].squeeze().numpy().T, aspect="auto", origin="lower")
+    result = vocex.model(item["mel"], inference=True)
+    pitch = result["measures"]["pitch"]
+    energy = result["measures"]["energy"]
+    va = result["measures"]["voice_activity_binary"]
+    mean_pitch = pitch.mean()
+    std_pitch = pitch.std()
+    pitch = (pitch - pitch.mean()) / pitch.std()
+    mean_energy = energy.mean()
+    std_energy = energy.std()
+    energy = (energy - energy.mean()) / energy.std()
+    va = (va - 0.5) * 2
+    durations = item["phone_durations"].squeeze().numpy()
+    plt.subplot(4, 1, 2)
+    sns.lineplot(
+        x=np.arange(len(pitch[0])),
+        y=pitch[0],
+        color="red",
+        label="Pitch",
+    )
+    sns.lineplot(
+        x=np.arange(len(energy[0])),
+        y=energy[0],
+        color="blue",
+        label="Energy",
+    )
+    sns.lineplot(
+        x=np.arange(len(va[0])),
+        y=va[0],
+        color="green",
+        label="Voice activity",
+    )
+    plt.legend()
+    dur = [d for d in durations if d > 0]
+    current_idx = 0
+    vpw = 5 # values per window
+    new_repr = np.zeros((len(dur), vpw*3 + 1))
+    for i, d in enumerate(dur):
+        new_repr[i, 0] = d
+        # get values in duration window
+        pitch_win = pitch[0, current_idx:current_idx+d]
+        energy_win = energy[0, current_idx:current_idx+d]
+        va_win = va[0, current_idx:current_idx+d]
+        current_idx += d
+        # resample to vpw values
+        pitch_win = resample(pitch_win, vpw)
+        energy_win = resample(energy_win, vpw)
+        va_win = resample(va_win, vpw)
+        new_repr[i, 1:vpw+1] = pitch_win
+        new_repr[i, vpw+1:2*vpw+1] = energy_win
+        new_repr[i, 2*vpw+1:3*vpw+1] = va_win
+    new_repr[:, 0] = np.log(new_repr[:, 0] + 1)
+    mean_dur = new_repr[:, 0].mean()
+    std_dur = new_repr[:, 0].std()
+    new_repr[:, 0] = (new_repr[:, 0] - mean_dur) / std_dur
+    plt.subplot(4, 1, 3)
+    # heatmap with log scale
+    phones = [idx2phone[int(p)] for i, p in enumerate(item["phones"][0]) if item["phone_durations"][0][i] > 0]
+    for p_i, p in enumerate(phones):
+        if "[" in p:
+            # make empty symbol for phones with []
+            phones[p_i] = ""
+    sns.heatmap(new_repr.T, cmap="viridis")
+    # set xticks while making sure they are in the middle of the phone
+    plt.tick_params(axis="x", which="both", bottom=False, top=False, labelbottom=True)
+    plt.xticks(np.arange(len(phones))+0.5, np.arange(len(phones)), rotation=0)
+    plt.yticks([0.5]+list(np.array([1,2,3])*(vpw)-vpw/2+1), ["Duration", "Pitch", "Energy", "Voice activity"], rotation=0)
+    plt.twiny()
+    plt.xticks(np.arange(len(phones))+0.5, phones, rotation=0)
+    plt.xlim(0, len(phones))
+    # allow some space between this plot and the next one
+    plt.subplots_adjust(hspace=0.5)
+    # reconstruct pitch, energy and va from new_repr
+    r_pitch = np.zeros(len(pitch[0]))
+    r_energy = np.zeros(len(energy[0]))
+    r_va = np.zeros(len(va[0]))
+    current_idx = 0
+    for i, d in enumerate(dur):
+        # get values in duration window
+        pitch_win = new_repr[i, 1:vpw+1]
+        energy_win = new_repr[i, vpw+1:2*vpw+1]
+        va_win = new_repr[i, 2*vpw+1:3*vpw+1]
+        # resample to d values
+        pitch_win = resample(pitch_win, d)
+        energy_win = resample(energy_win, d)
+        va_win = resample(va_win, d)
+        r_pitch[current_idx:current_idx+d] = pitch_win
+        r_energy[current_idx:current_idx+d] = energy_win
+        r_va[current_idx:current_idx+d] = va_win
+        current_idx += d
+    plt.subplot(4, 1, 4)
+    sns.lineplot(
+        x=np.arange(len(r_pitch)),
+        y=r_pitch,
+        color="red",
+        label="Pitch",
+    )
+    sns.lineplot(
+        x=np.arange(len(r_energy)),
+        y=r_energy,
+        color="blue",
+        label="Energy",
+    )
+    sns.lineplot(
+        x=np.arange(len(r_va)),
+        y=r_va,
+        color="green",
+        label="Voice activity",
+    )
+    plt.legend()
+    plt.savefig("test.png")
+    print("Mean pitch:", mean_pitch)
+    print("Std pitch:", std_pitch)
+    print("Mean energy:", mean_energy)
+    print("Std energy:", std_energy)
+    print("Mean duration:", mean_dur)
+    print("Std duration:", std_dur)
+    break