add option to provide audio samples for prediction

README.md

@@ -105,13 +105,18 @@ basic-pitch --help
 
 **predict()**
 
-Import `basic-pitch` into your own Python code and run the [`predict`](basic_pitch/inference.py) functions directly, providing an `<input-audio-path>` and returning the model's prediction results:
+Import `basic-pitch` into your own Python code and run the [`predict`](basic_pitch/inference.py) functions directly, providing an `<input-audio-path>` or an `<array-of-samples>` and returning the model's prediction results:
 
 ```python
 from basic_pitch.inference import predict
-from basic_pitch import ICASSP_2022_MODEL_PATH
 
+# get model predictions given an audio file
 model_output, midi_data, note_events = predict(<input-audio-path>)
+
+# or alternatively, provide an array of samples
+audio_array, sample_rate = librosa.load(<input-audio-path>, mono=True, duration=10.0, offset=5.0)
+model_output, midi_data, note_events = predict(audio_array, sample_rate)
+
 ```
 
 - `<minimum-frequency>` & `<maximum-frequency>` (*float*s) set the maximum and minimum allowed note frequency, in Hz, returned by the model. Pitch events with frequencies outside of this range will be excluded from the prediction results.

basic_pitch/inference.py

@@ -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
@@ -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])
@@ -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.
 
@@ -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)
 
@@ -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,
@@ -426,6 +441,7 @@ def predict(
     melodia_trick: bool = True,
     debug_file: Optional[pathlib.Path] = None,
     midi_tempo: float = 120,
+    verbose: bool = False
 ) -> Tuple[
     Dict[str, np.array],
     pretty_midi.PrettyMIDI,
@@ -434,7 +450,8 @@ 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.
@@ -449,9 +466,12 @@ def predict(
     """
 
     with no_tf_warnings():
-        print(f"Predicting MIDI for {audio_path}...")
+        if isinstance(audio_path_or_array, np.ndarray) and verbose:
+            print("Predicting MIDI ...")
+        elif verbose:
+            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,