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add option to provide audio samples for prediction #153

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add option to provide audio samples for prediction #153

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add option to provide audio samples for prediction
marypilataki Nov 20, 2024
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marypilataki Dec 2, 2024
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marypilataki Dec 3, 2024
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39 changes: 29 additions & 10 deletions basic_pitch/inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -213,7 +213,7 @@ def window_audio_file(


def get_audio_input(
audio_path: Union[pathlib.Path, str], overlap_len: int, hop_size: int
audio_path_or_array: Union[pathlib.Path, str, np.ndarray], sample_rate: int, overlap_len: int, hop_size: int
) -> Iterable[Tuple[npt.NDArray[np.float32], Dict[str, float], int]]:
"""
Read wave file (as mono), pad appropriately, and return as
Expand All @@ -228,8 +228,20 @@ def get_audio_input(

"""
assert overlap_len % 2 == 0, f"overlap_length must be even, got {overlap_len}"

audio_original, _ = librosa.load(str(audio_path), sr=AUDIO_SAMPLE_RATE, mono=True)
# if a numpy array of samples is provided, use it directly
if isinstance(audio_path_or_array, np.ndarray):
audio_original = audio_path_or_array
if sample_rate is None:
raise ValueError("Sample rate must be provided when input is an array of audio samples.")
# resample audio if required
elif sample_rate != AUDIO_SAMPLE_RATE:
audio_original = librosa.resample(audio_original, orig_sr=sample_rate, target_sr=AUDIO_SAMPLE_RATE)
# convert to mono if necessary
if audio_original.ndim != 1:
audio_original = librosa.to_mono(audio_path_or_array)
# load audio file
else:
audio_original, _ = librosa.load(str(audio_path_or_array), sr=AUDIO_SAMPLE_RATE, mono=True)

original_length = audio_original.shape[0]
audio_original = np.concatenate([np.zeros((int(overlap_len / 2),), dtype=np.float32), audio_original])
Expand Down Expand Up @@ -267,14 +279,16 @@ def unwrap_output(


def run_inference(
audio_path: Union[pathlib.Path, str],
audio_path_or_array: Union[pathlib.Path, str, np.ndarray],
sample_rate: None,
model_or_model_path: Union[Model, pathlib.Path, str],
debug_file: Optional[pathlib.Path] = None,
) -> Dict[str, np.array]:
"""Run the model on the input audio path.

Args:
audio_path: The audio to run inference on.
audio_path_or_array: The audio to run inference on. Can be either the path to an audio file or a numpy array of audio samples.
sample_rate: Sample rate of the audio file. Only used if audio_path_or_array is a np array.
model_or_model_path: A loaded Model or path to a serialized model to load.
debug_file: An optional path to output debug data to. Useful for testing/verification.

Expand All @@ -292,7 +306,7 @@ def run_inference(
hop_size = AUDIO_N_SAMPLES - overlap_len

output: Dict[str, Any] = {"note": [], "onset": [], "contour": []}
for audio_windowed, _, audio_original_length in get_audio_input(audio_path, overlap_len, hop_size):
for audio_windowed, _, audio_original_length in get_audio_input(audio_path_or_array, sample_rate, overlap_len, hop_size):
for k, v in model.predict(audio_windowed).items():
output[k].append(v)

Expand Down Expand Up @@ -415,7 +429,8 @@ def save_note_events(


def predict(
audio_path: Union[pathlib.Path, str],
audio_path_or_array: Union[pathlib.Path, str, np.ndarray],
sample_rate: int = None,
model_or_model_path: Union[Model, pathlib.Path, str] = ICASSP_2022_MODEL_PATH,
onset_threshold: float = 0.5,
frame_threshold: float = 0.3,
Expand All @@ -434,7 +449,8 @@ def predict(
"""Run a single prediction.

Args:
audio_path: File path for the audio to run inference on.
audio_path_or_array: File path for the audio to run inference on or array of audio samples.
sample_rate: Sample rate of the audio file. Only used if audio_path_or_array is a np array.
model_or_model_path: A loaded Model or path to a serialized model to load.
onset_threshold: Minimum energy required for an onset to be considered present.
frame_threshold: Minimum energy requirement for a frame to be considered present.
Expand All @@ -449,9 +465,12 @@ def predict(
"""

with no_tf_warnings():
print(f"Predicting MIDI for {audio_path}...")
if isinstance(audio_path_or_array, np.ndarray):
print("Predicting MIDI ...")
else:
print(f"Predicting MIDI for {audio_path_or_array}...")

model_output = run_inference(audio_path, model_or_model_path, debug_file)
model_output = run_inference(audio_path_or_array, sample_rate, model_or_model_path, debug_file)
min_note_len = int(np.round(minimum_note_length / 1000 * (AUDIO_SAMPLE_RATE / FFT_HOP)))
midi_data, note_events = infer.model_output_to_notes(
model_output,
Expand Down