diff --git a/riffusion/cli.py b/riffusion/cli.py
new file mode 100644
index 0000000..2022c05
--- /dev/null
+++ b/riffusion/cli.py
@@ -0,0 +1,141 @@
+"""
+Command line tools for riffusion.
+"""
+
+from pathlib import Path
+
+import argh
+import numpy as np
+from PIL import Image
+import pydub
+
+from riffusion.spectrogram_image_converter import SpectrogramImageConverter
+from riffusion.spectrogram_params import SpectrogramParams
+from riffusion.util import image_util
+
+
+@argh.arg("--step-size-ms", help="Duration of one pixel in the X axis of the spectrogram image")
+@argh.arg("--num-frequencies", help="Number of Y axes in the spectrogram image")
+def audio_to_image(
+    *,
+    audio: str,
+    image: str,
+    step_size_ms: int = 10,
+    num_frequencies: int = 512,
+    min_frequency: int = 0,
+    max_frequency: int = 10000,
+    window_duration_ms: int = 100,
+    padded_duration_ms: int = 400,
+    power_for_image: float = 0.25,
+    stereo: bool = False,
+    device: str = "cuda",
+):
+    """
+    Compute a spectrogram image from a waveform.
+    """
+    segment = pydub.AudioSegment.from_file(audio)
+
+    params = SpectrogramParams(
+        sample_rate=segment.frame_rate,
+        stereo=stereo,
+        window_duration_ms=window_duration_ms,
+        padded_duration_ms=padded_duration_ms,
+        step_size_ms=step_size_ms,
+        min_frequency=min_frequency,
+        max_frequency=max_frequency,
+        num_frequencies=num_frequencies,
+        power_for_image=power_for_image,
+    )
+
+    converter = SpectrogramImageConverter(params=params, device=device)
+
+    pil_image = converter.spectrogram_image_from_audio(segment)
+
+    pil_image.save(image, exif=pil_image.getexif(), format="PNG")
+    print(f"Wrote {image}")
+
+
+def print_exif(*, image: str) -> None:
+    """
+    Print the params of a spectrogram image as saved in the exif data.
+    """
+    pil_image = Image.open(image)
+    exif_data = image_util.exif_from_image(pil_image)
+
+    for name, value in exif_data.items():
+        print(f"{name:<20} = {value:>15}")
+
+
+def image_to_audio(*, image: str, audio: str, device: str = "cuda"):
+    """
+    Reconstruct an audio clip from a spectrogram image.
+    """
+    pil_image = Image.open(image)
+
+    # Get parameters from image exif
+    img_exif = pil_image.getexif()
+    assert img_exif is not None
+
+    try:
+        params = SpectrogramParams.from_exif(exif=img_exif)
+    except KeyError:
+        print("WARNING: Could not find spectrogram parameters in exif data. Using defaults.")
+        params = SpectrogramParams()
+
+    converter = SpectrogramImageConverter(params=params, device=device)
+    segment = converter.audio_from_spectrogram_image(pil_image)
+
+    extension = Path(audio).suffix[1:]
+    segment.export(audio, format=extension)
+
+    print(f"Wrote {audio} ({segment.duration_seconds:.2f} seconds)")
+
+
+def sample_clips(
+    *,
+    audio: str,
+    output_dir: str,
+    num_clips: int = 1,
+    duration_ms: int = 5000,
+    mono: bool = False,
+    extension: str = "wav",
+    seed: int = -1,
+):
+    """
+    Slice an audio file into clips of the given duration.
+    """
+    if seed >= 0:
+        np.random.seed(seed)
+
+    segment = pydub.AudioSegment.from_file(audio)
+
+    if mono:
+        segment = segment.set_channels(1)
+
+    output_dir_path = Path(output_dir)
+    if not output_dir_path.exists():
+        output_dir_path.mkdir(parents=True)
+
+    # TODO(hayk): Might be a lot easier with pydub
+    # https://github.com/jiaaro/pydub/blob/master/API.markdown#audiosegmentfrom_file
+
+    segment_duration_ms = int(segment.duration_seconds * 1000)
+    for i in range(num_clips):
+        clip_start_ms = np.random.randint(0, segment_duration_ms - duration_ms)
+        clip = segment[clip_start_ms : clip_start_ms + duration_ms]
+
+        clip_name = f"clip_{i}_start_{clip_start_ms}_ms_duration_{duration_ms}_ms.{extension}"
+        clip_path = output_dir_path / clip_name
+        clip.export(clip_path, format=extension)
+        print(f"Wrote {clip_path}")
+
+
+if __name__ == "__main__":
+    argh.dispatch_commands(
+        [
+            audio_to_image,
+            image_to_audio,
+            sample_clips,
+            print_exif,
+        ]
+    )
diff --git a/test/audio_to_image_test.py b/test/audio_to_image_test.py
new file mode 100644
index 0000000..4a84d76
--- /dev/null
+++ b/test/audio_to_image_test.py
@@ -0,0 +1,99 @@
+import typing as T
+
+import numpy as np
+from PIL import Image
+
+from riffusion.cli import audio_to_image
+from riffusion.spectrogram_params import SpectrogramParams
+
+from .test_case import TestCase
+
+
+class AudioToImageTest(TestCase):
+    """
+    Test riffusion.cli audio-to-image
+    """
+
+    @classmethod
+    def default_params(cls) -> T.Dict:
+        return dict(
+            step_size_ms=10,
+            num_frequencies=512,
+            # TODO(hayk): Change these to [20, 20000] once a model is updated
+            min_frequency=0,
+            max_frequency=10000,
+            stereo=False,
+            device=cls.DEVICE,
+        )
+
+    def test_audio_to_image(self) -> None:
+        """
+        Test audio-to-image with default params.
+        """
+        params = self.default_params()
+        self.helper_test_with_params(params)
+
+    def test_stereo(self) -> None:
+        """
+        Test audio-to-image with stereo=True.
+        """
+        params = self.default_params()
+        params["stereo"] = True
+        self.helper_test_with_params(params)
+
+    def helper_test_with_params(self, params: T.Dict) -> None:
+        audio_path = (
+            self.TEST_DATA_PATH
+            / "tired_traveler"
+            / "clips"
+            / "clip_2_start_103694_ms_duration_5678_ms.wav"
+        )
+        output_dir = self.get_tmp_dir("audio_to_image_")
+
+        if params["stereo"]:
+            stem = f"{audio_path.stem}_stereo"
+        else:
+            stem = audio_path.stem
+
+        image_path = output_dir / f"{stem}.png"
+
+        audio_to_image(audio=str(audio_path), image=str(image_path), **params)
+
+        # Check that the image exists
+        self.assertTrue(image_path.exists())
+
+        pil_image = Image.open(image_path)
+
+        # Check the image mode
+        self.assertEqual(pil_image.mode, "RGB")
+
+        # Check the image dimensions
+        duration_ms = 5678
+        self.assertTrue(str(duration_ms) in audio_path.name)
+        expected_image_width = round(duration_ms / params["step_size_ms"])
+        self.assertEqual(pil_image.width, expected_image_width)
+        self.assertEqual(pil_image.height, params["num_frequencies"])
+
+        # Get channels as numpy arrays
+        channels = [np.array(pil_image.getchannel(i)) for i in range(len(pil_image.getbands()))]
+        self.assertEqual(len(channels), 3)
+
+        if params["stereo"]:
+            # Check that the first channel is zero
+            self.assertTrue(np.all(channels[0] == 0))
+        else:
+            # Check that all channels are the same
+            self.assertTrue(np.all(channels[0] == channels[1]))
+            self.assertTrue(np.all(channels[0] == channels[2]))
+
+        # Check that the image has exif data
+        exif = pil_image.getexif()
+        self.assertIsNotNone(exif)
+        params_from_exif = SpectrogramParams.from_exif(exif)
+        expected_params = SpectrogramParams(
+            stereo=params["stereo"],
+            step_size_ms=params["step_size_ms"],
+            num_frequencies=params["num_frequencies"],
+            max_frequency=params["max_frequency"],
+        )
+        self.assertTrue(params_from_exif == expected_params)
diff --git a/test/image_to_audio_test.py b/test/image_to_audio_test.py
new file mode 100644
index 0000000..7ae1424
--- /dev/null
+++ b/test/image_to_audio_test.py
@@ -0,0 +1,71 @@
+from pathlib import Path
+
+import pydub
+
+from riffusion.cli import image_to_audio
+
+from .test_case import TestCase
+
+
+class ImageToAudioTest(TestCase):
+    """
+    Test riffusion.cli image-to-audio
+    """
+
+    def test_image_to_audio_mono(self) -> None:
+        self.helper_image_to_audio(
+            song_dir=self.TEST_DATA_PATH / "tired_traveler",
+            clip_name="clip_2_start_103694_ms_duration_5678_ms",
+            stereo=False,
+        )
+
+    def test_image_to_audio_stereo(self) -> None:
+        self.helper_image_to_audio(
+            song_dir=self.TEST_DATA_PATH / "tired_traveler",
+            clip_name="clip_2_start_103694_ms_duration_5678_ms",
+            stereo=True,
+        )
+
+    def helper_image_to_audio(self, song_dir: Path, clip_name: str, stereo: bool) -> None:
+        if stereo:
+            image_stem = clip_name + "_stereo"
+        else:
+            image_stem = clip_name
+
+        image_path = song_dir / "images" / f"{image_stem}.png"
+        output_dir = self.get_tmp_dir("image_to_audio_")
+        audio_path = output_dir / f"{image_path.stem}.wav"
+
+        image_to_audio(
+            image=str(image_path),
+            audio=str(audio_path),
+            device=self.DEVICE,
+        )
+
+        # Check that the audio exists
+        self.assertTrue(audio_path.exists())
+
+        # Load the reconstructed audio and the original clip
+        segment = pydub.AudioSegment.from_file(str(audio_path))
+        expected_segment = pydub.AudioSegment.from_file(
+            str(song_dir / "clips" / f"{clip_name}.wav")
+        )
+
+        # Check sample rate
+        self.assertEqual(segment.frame_rate, expected_segment.frame_rate)
+
+        # Check duration
+        actual_duration_ms = round(segment.duration_seconds * 1000)
+        expected_duration_ms = round(expected_segment.duration_seconds * 1000)
+        self.assertTrue(abs(actual_duration_ms - expected_duration_ms) < 10)
+
+        # Check the number of channels
+        self.assertEqual(expected_segment.channels, 2)
+        if stereo:
+            self.assertEqual(segment.channels, 2)
+        else:
+            self.assertEqual(segment.channels, 1)
+
+
+if __name__ == "__main__":
+    TestCase.main()
diff --git a/test/print_exif_test.py b/test/print_exif_test.py
new file mode 100644
index 0000000..82b6d94
--- /dev/null
+++ b/test/print_exif_test.py
@@ -0,0 +1,32 @@
+import contextlib
+import io
+
+from riffusion.cli import print_exif
+
+from .test_case import TestCase
+
+
+class PrintExifTest(TestCase):
+    """
+    Test riffusion.cli print-exif
+    """
+
+    def test_print_exif(self) -> None:
+        """
+        Test print-exif.
+        """
+        image_path = (
+            self.TEST_DATA_PATH
+            / "tired_traveler"
+            / "images"
+            / "clip_2_start_103694_ms_duration_5678_ms.png"
+        )
+
+        # Redirect stdout
+        stdout = io.StringIO()
+        with contextlib.redirect_stdout(stdout):
+            print_exif(image=str(image_path))
+
+        # Check that a couple of values are printed
+        self.assertTrue("NUM_FREQUENCIES: 512" in stdout.getvalue())
+        self.assertTrue("SAMPLE_RATE: 44100" in stdout.getvalue())
diff --git a/test/sample_clips_test.py b/test/sample_clips_test.py
new file mode 100644
index 0000000..4b3080a
--- /dev/null
+++ b/test/sample_clips_test.py
@@ -0,0 +1,88 @@
+import typing as T
+
+import pydub
+
+from riffusion.cli import sample_clips
+
+from .test_case import TestCase
+
+
+class SampleClipsTest(TestCase):
+    """
+    Test riffusion.cli sample-clips
+    """
+
+    @staticmethod
+    def default_params() -> T.Dict:
+        return dict(
+            num_clips=3,
+            duration_ms=5678,
+            mono=False,
+            extension="wav",
+            seed=42,
+        )
+
+    def test_sample_clips(self) -> None:
+        """
+        Test sample-clips with default params.
+        """
+        params = self.default_params()
+        self.helper_test_with_params(params)
+
+    def test_mono(self) -> None:
+        """
+        Test sample-clips with mono=True.
+        """
+        params = self.default_params()
+        params["mono"] = True
+        params["num_clips"] = 1
+        self.helper_test_with_params(params)
+
+    def test_mp3(self) -> None:
+        """
+        Test sample-clips with extension=mp3.
+        """
+        if pydub.AudioSegment.converter is None:
+            self.skipTest("skipping, ffmpeg not found")
+
+        params = self.default_params()
+        params["extension"] = "mp3"
+        params["num_clips"] = 1
+        self.helper_test_with_params(params)
+
+    def helper_test_with_params(self, params: T.Dict) -> None:
+        """
+        Test sample-clips with the given params.
+        """
+        audio_path = self.TEST_DATA_PATH / "tired_traveler" / "tired_traveler.mp3"
+        output_dir = self.get_tmp_dir("sample_clips_")
+
+        sample_clips(
+            audio=str(audio_path),
+            output_dir=str(output_dir),
+            **params,
+        )
+
+        # For each file in output dir
+        counter = 0
+        for clip_path in output_dir.iterdir():
+            # Check that it has the right extension
+            self.assertEqual(clip_path.suffix, f".{params['extension']}")
+
+            # Check that it has the right duration
+            segment = pydub.AudioSegment.from_file(clip_path)
+            self.assertEqual(round(segment.duration_seconds * 1000), params["duration_ms"])
+
+            # Check that it has the right number of channels
+            if params["mono"]:
+                self.assertEqual(segment.channels, 1)
+            else:
+                self.assertEqual(segment.channels, 2)
+
+            counter += 1
+
+        self.assertEqual(counter, params["num_clips"])
+
+
+if __name__ == "__main__":
+    TestCase.main()