diff --git a/lhotse/recipes/__init__.py b/lhotse/recipes/__init__.py
index c7121f490..a3aec3ee9 100644
--- a/lhotse/recipes/__init__.py
+++ b/lhotse/recipes/__init__.py
@@ -1,4 +1,5 @@
 from .aishell import prepare_aishell
+from .timit import download_timit, prepare_timit
 from .ami import download_ami, prepare_ami
 from .babel import prepare_single_babel_language
 from .broadcast_news import prepare_broadcast_news
diff --git a/lhotse/recipes/timit.py b/lhotse/recipes/timit.py
new file mode 100644
index 000000000..ec9505cb4
--- /dev/null
+++ b/lhotse/recipes/timit.py
@@ -0,0 +1,294 @@
+#!/usr/bin/env python3
+
+# Copyright 2021 Xiaomi Corporation (Author: Mingshuang Luo)
+# Apache 2.0
+
+from collections import defaultdict
+
+import os
+import zipfile
+import logging
+from tqdm import tqdm
+from pathlib import Path
+from typing import Dict, Optional, Union
+from concurrent.futures.thread import ThreadPoolExecutor
+
+from lhotse import validate_recordings_and_supervisions
+from lhotse.audio import Recording, RecordingSet
+from lhotse.supervision import SupervisionSegment, SupervisionSet
+from lhotse.utils import Pathlike, urlretrieve_progress
+
+def download_timit(
+        target_dir: Pathlike = '.',
+        force_download: bool = False,
+        base_url: Optional[str] = 'https://data.deepai.org/timit.zip') -> None:
+    """
+    Download and unzip the dataset TIMIT.
+    :param target_dir: Pathlike, the path of the dir to store the dataset.
+    :param force_download: bool, if True, download the zips no matter if the zips exists.
+    :param base_url: str, the URL of the TIMIT dataset to download.
+    """
+    target_dir = Path(target_dir)
+    target_dir.mkdir(parents=True, exist_ok=True)
+    tar_name = 'timit.zip'
+    tar_path = target_dir / tar_name
+    if force_download or not tar_path.is_file():
+        urlretrieve_progress(f'{base_url}', filename=tar_path, desc=f'Downloading and unzip {tar_name}')
+    
+    zip_file = zipfile.ZipFile(tar_path)
+    if os.path.isdir(tar_name[:-4]):
+        pass
+    else:
+        os.mkdir(tar_name[:-4])
+    for names in zip_file.namelist():
+        zip_file.extract(names, tar_name[:-4])
+    zip_file.close()
+
+def prepare_timit(
+        corpus_dir: Pathlike,
+        splits_dir: Pathlike,
+        output_dir: Optional[Pathlike] = None,
+        num_phones: int = 48,
+        num_jobs: int = 1
+) -> Dict[str, Dict[str, Union[RecordingSet, SupervisionSet]]]:
+    """
+    Returns the manifests which consists of the Recodings and Supervisions.
+    :param corpus_dir: Pathlike, the path of the data dir. 
+    :param splits_dir: Pathlike, the path of the txt files for data division (train, dev, tst).
+    :param output_dir: Pathlike, the path where to write and save the manifests.
+    :param num_phones: int=48, the number of phones (60, 48 or 39) for modeling and 48 is regarded as the default value.
+    :return: a Dict whose key is the dataset part, and the value is Dicts with the keys 'audio' and 'supervisions'.
+    """
+    corpus_dir = Path(corpus_dir)
+    assert corpus_dir.is_dir(), f'No such directory: {corpus_dir}'
+
+    splits_dir = Path(splits_dir)
+    assert splits_dir.is_dir(), f'No such directory: {splits_dir}'
+
+    if output_dir is not None:
+        output_dir = Path(output_dir)
+        output_dir.mkdir(parents=True, exist_ok=True)
+
+    manifests = defaultdict(dict)
+    dataset_parts = ['TRAIN', 'DEV', 'TEST']
+    
+    phones_dict = {}
+
+    if num_phones in [60, 48, 39]:
+        phones_dict = get_phonemes(num_phones)
+    else:
+        raise ValueError("The value of num_phones must be in [60, 48, 39].")
+
+    with ThreadPoolExecutor(num_jobs) as ex:
+        for part in dataset_parts:
+            file_name = ''
+
+            if part == 'TRAIN': 
+                file_name = splits_dir / 'train_samples.txt'
+            elif part == 'DEV':
+                file_name = splits_dir / 'dev_samples.txt'
+            else:
+                file_name = splits_dir / 'tst_samples.txt'
+            wav_files = []
+            with open(file_name, 'r') as f:
+                lines = f.readlines() 
+                for line in lines:
+                    items = line.strip().split(' ')
+                    wav = corpus_dir / items[-1]
+                    wav_files.append(wav)
+                logging.debug(f'{part} dataset manifest generation.')
+                recordings = []
+                supervisions = []
+
+                for wav_file in tqdm(wav_files):
+                    items = str(wav_file).strip().split('/')
+                    idx = items[-2] + '-' + items[-1][:-4]
+                    speaker = items[-2] 
+                    transcript_file = Path(wav_file).with_suffix('.PHN')
+                    if not Path(wav_file).is_file():
+                        logging.warning(f'No such file: {wav_file}')
+                        continue
+                    if not Path(transcript_file).is_file():
+                        logging.warning(f'No transcript: {transcript_file}')
+                        continue
+                    text = []
+                    with open(transcript_file, 'r') as f:
+                        lines = f.readlines()
+                        for line in lines:
+                            phone = line.rstrip('\n').split(' ')[-1]
+                            if num_phones != 60: phone = phones_dict[str(phone)]
+                            text.append(phone)
+
+                        text = ' '.join(text).replace('h#', 'sil')
+
+                    recording = Recording.from_file(path=wav_file, recording_id=idx)
+                    recordings.append(recording)
+                    segment = SupervisionSegment(
+                        id=idx,
+                        recording_id=idx,
+                        start=0.0,
+                        duration=recording.duration,
+                        channel=0,
+                        language='English',
+                        speaker=speaker,
+                        text=text.strip())
+
+                    supervisions.append(segment)
+
+                    recording_set = RecordingSet.from_recordings(recordings)
+                    supervision_set = SupervisionSet.from_segments(supervisions)
+                    validate_recordings_and_supervisions(recording_set, supervision_set)
+
+                    if output_dir is not None:
+                        supervision_set.to_json(output_dir / f'supervisions_{part}.json')
+                        recording_set.to_json(output_dir / f'recordings_{part}.json')
+
+                    manifests[part] = {
+                        'recordings': recording_set,
+                        'supervisions': supervision_set}
+
+    return manifests
+
+def get_phonemes(num_phones):
+    """
+    Choose and convert the phones for modeling.
+    :param num_phones: the number of phones for modeling. 
+    """
+    phonemes = {}
+   
+    if num_phones == int(48):
+        logging.debug("Using 48 phones for modeling!")
+        # This dictionary is used to convert the 60 phoneme set into the 48 one.
+        phonemes["sil"] = "sil"
+        phonemes["aa"] = "aa"
+        phonemes["ae"] = "ae"
+        phonemes["ah"] = "ah"
+        phonemes["ao"] = "ao"
+        phonemes["aw"] = "aw"
+        phonemes["ax"] = "ax"
+        phonemes["ax-h"] = "ax"
+        phonemes["axr"] = "er"
+        phonemes["ay"] = "ay"
+        phonemes["b"] = "b"
+        phonemes["bcl"] = "vcl"
+        phonemes["ch"] = "ch"
+        phonemes["d"] = "d"
+        phonemes["dcl"] = "vcl"
+        phonemes["dh"] = "dh"
+        phonemes["dx"] = "dx"
+        phonemes["eh"] = "eh"
+        phonemes["el"] = "el"
+        phonemes["em"] = "m"
+        phonemes["en"] = "en"
+        phonemes["eng"] = "ng"
+        phonemes["epi"] = "epi"
+        phonemes["er"] = "er"
+        phonemes["ey"] = "ey"
+        phonemes["f"] = "f"
+        phonemes["g"] = "g"
+        phonemes["gcl"] = "vcl"
+        phonemes["h#"] = "sil"
+        phonemes["hh"] = "hh"
+        phonemes["hv"] = "hh"
+        phonemes["ih"] = "ih"
+        phonemes["ix"] = "ix"
+        phonemes["iy"] = "iy"
+        phonemes["jh"] = "jh"
+        phonemes["k"] = "k"
+        phonemes["kcl"] = "cl"
+        phonemes["l"] = "l"
+        phonemes["m"] = "m"
+        phonemes["n"] = "n"
+        phonemes["ng"] = "ng"
+        phonemes["nx"] = "n"
+        phonemes["ow"] = "ow"
+        phonemes["oy"] = "oy"
+        phonemes["p"] = "p"
+        phonemes["pau"] = "sil"
+        phonemes["pcl"] = "cl"
+        phonemes["q"] = ""
+        phonemes["r"] = "r"
+        phonemes["s"] = "s"
+        phonemes["sh"] = "sh"
+        phonemes["t"] = "t"
+        phonemes["tcl"] = "cl"
+        phonemes["th"] = "th"
+        phonemes["uh"] = "uh"
+        phonemes["uw"] = "uw"
+        phonemes["ux"] = "uw"
+        phonemes["v"] = "v"
+        phonemes["w"] = "w"
+        phonemes["y"] = "y"
+        phonemes["z"] = "z"
+        phonemes["zh"] = "zh"
+
+    elif num_phones == int(39):
+        logging.debug("Using 39 phones for modeling!")
+        # This dictionary is used to convert the 60 phoneme set into the 39 one.
+        phonemes["sil"] = "sil"
+        phonemes["aa"] = "aa"
+        phonemes["ae"] = "ae"
+        phonemes["ah"] = "ah"
+        phonemes["ao"] = "aa"
+        phonemes["aw"] = "aw"
+        phonemes["ax"] = "ah"
+        phonemes["ax-h"] = "ah"
+        phonemes["axr"] = "er"
+        phonemes["ay"] = "ay"
+        phonemes["b"] = "b"
+        phonemes["bcl"] = "sil"
+        phonemes["ch"] = "ch"
+        phonemes["d"] = "d"
+        phonemes["dcl"] = "sil"
+        phonemes["dh"] = "dh"
+        phonemes["dx"] = "dx"
+        phonemes["eh"] = "eh"
+        phonemes["el"] = "l"
+        phonemes["em"] = "m"
+        phonemes["en"] = "n"
+        phonemes["eng"] = "ng"
+        phonemes["epi"] = "sil"
+        phonemes["er"] = "er"
+        phonemes["ey"] = "ey"
+        phonemes["f"] = "f"
+        phonemes["g"] = "g"
+        phonemes["gcl"] = "sil"
+        phonemes["h#"] = "sil"
+        phonemes["hh"] = "hh"
+        phonemes["hv"] = "hh"
+        phonemes["ih"] = "ih"
+        phonemes["ix"] = "ih"
+        phonemes["iy"] = "iy"
+        phonemes["jh"] = "jh"
+        phonemes["k"] = "k"
+        phonemes["kcl"] = "sil"
+        phonemes["l"] = "l"
+        phonemes["m"] = "m"
+        phonemes["ng"] = "ng"
+        phonemes["n"] = "n"
+        phonemes["nx"] = "n"
+        phonemes["ow"] = "ow"
+        phonemes["oy"] = "oy"
+        phonemes["p"] = "p"
+        phonemes["pau"] = "sil"
+        phonemes["pcl"] = "sil"
+        phonemes["q"] = ""
+        phonemes["r"] = "r"
+        phonemes["s"] = "s"
+        phonemes["sh"] = "sh"
+        phonemes["t"] = "t"
+        phonemes["tcl"] = "sil"
+        phonemes["th"] = "th"
+        phonemes["uh"] = "uh"
+        phonemes["uw"] = "uw"
+        phonemes["ux"] = "uw"
+        phonemes["v"] = "v"
+        phonemes["w"] = "w"
+        phonemes["y"] = "y"
+        phonemes["z"] = "z"
+        phonemes["zh"] = "sh"
+    
+    else:
+        logging.debug("Using 60 phones for modeling!")
+
+    return phonemes