id string | lang_id int32 | phonemes string | mel_shape list | mel unknown |
|---|---|---|---|---|
Akuapem_Twi_twi_00041924 | 0 | balˈak bˈo fˈuw bˌalaˈam. | [
80,
86
] | [
147,
78,
85,
77,
80,
89,
1,
0,
118,
0,
123,
39,
100,
101,
115,
99,
114,
39,
58,
32,
39,
60,
102,
52,
39,
44,
32,
39,
102,
111,
114,
116,
114,
97,
110,
95,
111,
114,
100,
101,
114,
39,
58,
32,
70,
97,
108,
115,
101,
44,... |
Akuapem_Twi_twi_00039298 | 0 | ˈama nˈe fapˈem hˈo ˈada hˈo. | [
80,
86
] | [
147,
78,
85,
77,
80,
89,
1,
0,
118,
0,
123,
39,
100,
101,
115,
99,
114,
39,
58,
32,
39,
60,
102,
52,
39,
44,
32,
39,
102,
111,
114,
116,
114,
97,
110,
95,
111,
114,
100,
101,
114,
39,
58,
32,
70,
97,
108,
115,
101,
44,... |
Akuapem_Twi_twi_00021276 | 0 | ˌasoɾedˈan no ˌanuonjˈam. | [
80,
86
] | [
147,
78,
85,
77,
80,
89,
1,
0,
118,
0,
123,
39,
100,
101,
115,
99,
114,
39,
58,
32,
39,
60,
102,
52,
39,
44,
32,
39,
102,
111,
114,
116,
114,
97,
110,
95,
111,
114,
100,
101,
114,
39,
58,
32,
70,
97,
108,
115,
101,
44,... |
Akuapem_Twi_twi_00024571 | 0 | nˈanso woɾˈenhu wˈon. | [
80,
86
] | [
147,
78,
85,
77,
80,
89,
1,
0,
118,
0,
123,
39,
100,
101,
115,
99,
114,
39,
58,
32,
39,
60,
102,
52,
39,
44,
32,
39,
102,
111,
114,
116,
114,
97,
110,
95,
111,
114,
100,
101,
114,
39,
58,
32,
70,
97,
108,
115,
101,
44,... |
Akuapem_Twi_twi_00032129 | 0 | nˈa wopˈam no fˈi wiˈase. | [
80,
86
] | [
147,
78,
85,
77,
80,
89,
1,
0,
118,
0,
123,
39,
100,
101,
115,
99,
114,
39,
58,
32,
39,
60,
102,
52,
39,
44,
32,
39,
102,
111,
114,
116,
114,
97,
110,
95,
111,
114,
100,
101,
114,
39,
58,
32,
70,
97,
108,
115,
101,
44,... |
Akuapem_Twi_twi_00025892 | 0 | nokwˈaɾe mmˌuadˈadi. | [
80,
86
] | "k05VTVBZAQB2AHsnZGVzY3InOiAnPGY0JywgJ2ZvcnRyYW5fb3JkZXInOiBGYWxzZSwgJ3NoYXBlJzogKDgwLCA4NiksIH0gICA(...TRUNCATED) |
Akuapem_Twi_twi_00001506 | 0 | nˈa sˈimɾi wˈoo mˈosa. | [
80,
86
] | "k05VTVBZAQB2AHsnZGVzY3InOiAnPGY0JywgJ2ZvcnRyYW5fb3JkZXInOiBGYWxzZSwgJ3NoYXBlJzogKDgwLCA4NiksIH0gICA(...TRUNCATED) |
Akuapem_Twi_twi_00000577 | 0 | hˈeles wˈoo ˌeleˈasa. | [
80,
86
] | "k05VTVBZAQB2AHsnZGVzY3InOiAnPGY0JywgJ2ZvcnRyYW5fb3JkZXInOiBGYWxzZSwgJ3NoYXBlJzogKDgwLCA4NiksIH0gICA(...TRUNCATED) |
Akuapem_Twi_twi_00047081 | 0 | ˈem ɪdʒˈɛktɪv a tˈe a ˈem ˈef o anˈim. | [
80,
86
] | "k05VTVBZAQB2AHsnZGVzY3InOiAnPGY0JywgJ2ZvcnRyYW5fb3JkZXInOiBGYWxzZSwgJ3NoYXBlJzogKDgwLCA4NiksIH0gICA(...TRUNCATED) |
Akuapem_Twi_twi_00011993 | 0 | asˈase sˈo nnˈipa njinˈaa. | [
80,
86
] | "k05VTVBZAQB2AHsnZGVzY3InOiAnPGY0JywgJ2ZvcnRyYW5fb3JkZXInOiBGYWxzZSwgJ3NoYXBlJzogKDgwLCA4NiksIH0gICA(...TRUNCATED) |
End of preview. Expand in Data Studio
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Ghana Speech Prepped (Filtered)
Precomputed mel spectrograms + phonemized text for multilingual Matcha-TTS training, filtered to clips in [1.0, 10.0] seconds and capped at 10 hours per language.
Stats
| Metric | Value |
|---|---|
| Train clips | 485,575 |
| Val clips | 1,344 |
| Total train audio | 389.4 hours |
| Languages | 42 |
| Mel mean | -5.277032676706492 |
| Mel std | 3.064793980356 |
| Duration filter | [1.0, 10.0] seconds |
| Cap | 10 hours per language |
Format
Each Parquet shard contains:
| Column | Type | Description |
|---|---|---|
id |
string | Clip identifier (wav stem) |
lang_id |
int32 | Language ID (0-41), repurposes speaker slot |
phonemes |
string | LFN phonemized text |
mel_shape |
list[int32] | Shape of the mel spectrogram |
mel |
binary | Raw numpy bytes of the (un-normalized) mel spectrogram |
Reconstruction
import io, json, numpy as np
from pathlib import Path
from datasets import load_dataset
from huggingface_hub import hf_hub_download
# Download metadata
stats = json.loads(hf_hub_download("ghananlpcommunity/ghana-speech-prepped-filtered", "ghana_speech_filtered.json", repo_type="dataset"))
lang_map = json.loads(hf_hub_download("ghananlpcommunity/ghana-speech-prepped-filtered", "lang_map.json", repo_type="dataset"))
# Load parquet shards
ds = load_dataset("ghananlpcommunity/ghana-speech-prepped-filtered")
# Reconstruct mel directory + filelists
out_dir = Path("ghana_speech_data")
mels_dir = out_dir / "mels"
mels_dir.mkdir(parents=True, exist_ok=True)
train_lines, val_lines = [], []
for split, lines in [("train", train_lines), ("val", val_lines)]:
for row in ds[split]:
mel = np.load(io.BytesIO(row["mel"]))
np.save(mels_dir / f"{row['id']}.npy", mel)
wav_path = str(out_dir / "wavs" / f"{row['id']}.wav")
lines.append(f"{wav_path}|{row['lang_id']}|{row['phonemes']}")
(out_dir / "train_filtered.txt").write_text("\n".join(train_lines))
(out_dir / "val_filtered.txt").write_text("\n".join(val_lines))
Source
- Original dataset:
ghananlpcommunity/ghana-speech(42 language subsets, ~1.41M clips) - Phonemization: espeak
lfnvoice viatwi_cleaners - Mel: n_fft=1024, n_mels=80, sample_rate=22050, hop_length=256, win_length=1024, f_min=0, f_max=8000
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