Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

Unreleased

Added

• AcousticModelV3_0, GeoModelV3_0, AcousticSessionBase and the vocabulary of load(..) are importable from birdnet: the Literal types (ACOUSTIC_MODEL_VERSIONS, MODEL_LANGUAGES, MODEL_PRECISIONS, …), the VALID_* lists (typed, and VALID_MODEL_LANGUAGES without its duplicates) and the defaults MODEL_PRECISION_FP32, MODEL_LANGUAGE_EN_US and LIBRARY_TF_DEFAULT, so typed callers no longer need birdnet.globals (#123).

• CustomTFLiteInfo, which inspect_custom_tflite(..) returns, reports input_samples: the input length a classifier file fixes (96,000 samples for a 3.0 export, 144,000 for 2.4), or None when it takes any length, as the official 3.0 TFLite release does. An inspection result no longer compares equal to a CustomTFLiteInfo built without it (#129).

Bugfixes

• The type stub had no overload for the documented load("acoustic", "3.0", "tf") and load("acoustic", "3.0", "pb") calls and rejected precision="fp16" for the acoustic 3.0 onnx backend, so type checkers reported false errors and inferred Any; it is now in sync with the loader, which also means library="litert" for the geo 3.0 tf backend, always refused at runtime, is now a type error, and load_custom(.., "auto", "tf", ..) accepts every precision a detected 2.4 classifier takes (#123).

• The quick-start example imported AudioDataset, a name that does not exist in the package, and raised ImportError when copied; the same page now lists pt among the geo 3.0 backends, and the setup page names the full 3.11-3.14 test matrix (#123, from #108).

• Each acoustic session left its log handlers attached to loggers that live as long as the process, one of them holding the session’s closed logging queue, so a long-running process accumulated them with every session, and entering the same session object a second time hit the stale handlers and lost its log; leaving a session now detaches them (#125).

• The app data directory was resolved once, when birdnet was imported, so BIRDNET_APP_DATA set after the import was ignored and models, labels and the taxonomy still went to the old location; it is now resolved whenever a path is needed, and importing the package no longer creates the directory (#125).

• The acoustic to_csv(..) wrote a start or end time whose seconds round up to a whole minute as 60.00 seconds of the previous minute (59.996 s became 00:00:60.00), and left a double quote inside an input path or species name unescaped, which broke the row for CSV readers; such times now carry into the next minute (00:01:00.00) and quotes are doubled as RFC 4180 requires (#126).

• The geo 3.0 tf backend’s TensorFlow 2.18/2.19 check let versions such as 2.180 through and ran only when the model was first used, after the model download and label generation; load(..) and load_custom(..) now raise a RuntimeError for an unsupported TensorFlow before anything is downloaded, which for load_custom(..) with check_validity=True replaces the ValueError its model check raised (#126).

• An acoustic encoding session accepted a non-numeric speed, which surfaced only at run(..) as “Analysis was cancelled”, a speed outside [0.01, 100], which it used as given, and a progress_callback without show_stats="progress" or "benchmark", which was then never called; all three are now rejected when the session is created, as prediction sessions already did. A prediction session likewise rejects a default_confidence_threshold that is not a number, including a numeric string such as "0.5" that used to be converted silently (#126).

• When load_custom(..) could not open a model, the check subprocess also printed Error loading model: .. to stderr, the raised ValueError was not chained to the original error, and a crashed check reported only Failed to load model.; the error is now raised once, chained, with the underlying message (and its type when it is not a builtin exception), and inspect_custom_tflite(..) no longer prints its error before raising it (#126).

• A run that failed in the calling process, for example because an on_file_complete or progress callback raised or an input had more segments than supported, ended with only “Analysis was cancelled. Please check the logs”; that error now starts with the original exception’s type and message and chains it as the cause. Failures inside the pipeline’s child processes still point to the log (#126).

• Numeric parameters rejected NumPy scalars other than np.float64 with a TypeError, for example an np.float32 value in custom_confidence_thresholds or an np.int64 week taken from np.arange; the acoustic speed, overlap_duration_s, max_audio_duration_min, sigmoid_sensitivity and custom confidence thresholds and the geo latitude, longitude and week now accept them (#126).

• The acoustic 3.0 pb backend and the custom 3.0 classifier exports (TFLite and Raven) take only 3.0 s segments, but accepted any segment_size_s and then failed mid-run with only “Analysis was cancelled”, pointing to a log without the cause; a session now raises a ValueError for another length when it is created. A custom TFLite export is recognized when load_custom(..) inspects it, which it does unless check_validity=False is passed with an explicit version (#129).

• A segment_size_s that is not a whole number of seconds, such as 2.5, failed every acoustic 3.0 run with “Analysis was cancelled”, because segments were padded to the whole seconds only; such lengths now run on the exports that take other lengths, and the model’s input width is rounded instead of truncated, so 2.01 s at 32 kHz is 64,320 samples, not 64,319 (#129).

• sigmoid_sensitivity=None raised a TypeError whenever apply_sigmoid=True, so Perch V2’s predict(.., apply_sigmoid=True) failed unless a sensitivity was passed as well, its default being None; None now means the plain sigmoid (sensitivity 1.0) for every acoustic model (#129).

• Two processes downloading the same zip-packaged model at once (a pb model, Perch V2, the 2.4 geo tf model, or two precisions of the 2.4 acoustic tf model, whose labels share one directory) each wiped the other’s freshly extracted files, and one of them then failed its availability check; a download now holds a lock while it runs, a second process waits for it and reuses its result, and a 2.4 acoustic tf precision that is already usable keeps its labels while a sibling downloads. The wait fails only after five minutes without a sign of life from the downloading process. Extraction also no longer prints “Extracting…” and “Extracted.” to stdout (#130).

Performance

• to_arrow_table() and to_parquet(..) of encoding results built the embedding column from one Python float per value; it is now built from the NumPy buffer, so 20,000 embeddings of 1,024 values convert in about 0.1 s instead of 1.5 s (#124).

• A geo session rebuilt the species-name and species-id arrays (14,082 entries for V3.0) on every run(..), about half the time of a single-week call with the onnx backend; the session now builds them once and hands every result the same read-only arrays (#124).

• to_csv(..) formatted every embedding value as a NumPy scalar and advanced the progress bar once per row; values are now formatted as Python floats and the bar advances once per written block, which makes the CSV export of encoding results about 1.7× faster with identical output (#124).

• The producer stacked each batch of audio segments into a temporary array before copying it into shared memory; it now stacks straight into the ring slot, which cuts writing a batch of 32 segments from about 6.5 ms to 2.2 ms (#124).

1.3.1 - 2026-10-18

Bugfixes

• CPU inference with the onnx, pt and pb backends started one intra-op thread per physical core in every worker, and n_workers defaults to the number of physical cores, so an 8-core machine ran 64 busy threads. Each CPU worker now gets its share of the cores, which makes a default 3.0 onnx session about 8× faster on an 8-core Linux machine and 5–10× faster on a 10-core Windows laptop; detections are unchanged and scores move by at most 1.5e-6 (#120).

• Loading a second result from .npz (AcousticFilePredictionResult.load(..) and the other result classes) silently replaced the arrays of every result loaded earlier in the same process, because the loader stored them on the class instead of the instance; each loaded result now owns its data (#116).

• GeoPredictionResult.latitude and .longitude truncated the coordinates to integers, so predict(42.5, -76.45, week=4) reported 42 and -76; they are now floats (#116).

• encode_arrays(..) with show_stats set raised a TypeError after inference had finished, while the summary tried to list the file formats of in-memory inputs that have none (#116).

• encode(..) and encode_arrays(..) with show_stats="benchmark" raised a TypeError while writing the benchmark summary, which lacked the start method the prediction summary records (#116).

• The geo models ignored half_precision=True and returned float32 scores as with False; the session now applies the cast as the acoustic workers do (#116).

• An input with more segments than the pipeline’s segment index can address (its capacity is max_audio_duration_min, when set, rounded up to the index dtype) was reported by the producer as valid with zero segments, which could reach on_file_complete as an empty result before the run was cancelled; the producer now marks the input unprocessable and cancels the run, as the input analyzer does (#116).

• The n_workers docstrings of acoustic predict(..) no longer describe a worker killed mid-batch as deadlocking the run, which 1.1.0 fixed (#116).

• An audio array with more than two dimensions passed to predict_arrays(..) or encode_arrays(..) failed in the producer with a bare AssertionError, because the reader’s dimension check joined its conditions with and and could never fire; the producer now fails with a ValueError naming the number of dimensions (#117).

• The benchmark statistics (show_stats="benchmark") reported speed_worker_xrt_max as 0.0 on every run, because the value was hardcoded; it is now the highest worker xRT sampled during the run (#117).

• Reading an MP3 file slowed down with the square of its length: the producer reopened the file and seeked to every 3 s window, and libsndfile seeks in an MP3 by decoding from its start. Each file is now read once, front to back, which makes a 60 min MP3 about 7× faster; WAV, FLAC and AIFF input is unchanged, Ogg Vorbis and Opus windows now equal a decode of the whole file, and MP3 windows move by at most 2e-8 (#119).

1.3.0 - 2026-10-16

Changed

• sigmoid_sensitivity now applies to the models that apply the sigmoid inside their graph: the 3.0 models on every backend, custom 3.0 classifiers and Raven exports on either base. Any value other than 1.0 used to raise a ValueError for them, with or without apply_sigmoid. The prediction worker recovers the logits from such a model’s scores and runs the pipeline’s sigmoid on them, so a sensitivity moves their scores and thresholds as it moves a 2.4 model’s, to within float32 rounding (at most 1.3e-5 at sensitivity 0.5); the default leaves the scores unchanged, apply_sigmoid=False ignores the sensitivity as it does for the 2.4 models, and apply_softmax=True still raises (#114).

1.2.0 - 2026-10-16

Added

• Custom classifiers that BirdNET-Analyzer trains on the BirdNET 3.0 base now load and run: birdnet.load_custom("acoustic", "3.0", "tf", model, labels) reads the export’s predictions and embeddings outputs by name, so both predict(..) and encode(..) work on replace and append exports, and is_raven=True loads the analyzer’s 3.0 Raven SavedModel (basic signature, sigmoid in the model, no embeddings). Previously the 3.0 TFLite export passed the validity check and then crashed the worker process, and the 3.0 Raven export did not load at all (#111).

• Added birdnet.inspect_custom_tflite(model), which reports a custom TFLite classifier’s base version ("2.4" or "3.0", derived from the input length and the embeddings width), whether it replaces or appends to the official species, its number of species, whether it applies the sigmoid itself and whether it exposes embeddings. It runs in a subprocess, so the calling process never imports TensorFlow. load_custom accepts version="auto" for acoustic TFLite classifiers and uses it (#111).

Changed

• Raven exports (load_custom(.., "pb", .., is_raven=True)) apply the sigmoid inside their graph, and predict(..) now returns those scores unchanged. The 2.4 Raven path applied the pipeline’s sigmoid on top, which squashed every score into 0.5–0.73; scores from a 2.4 Raven classifier therefore change with this release, to the values the same classifier gives as a TFLite export. As for the 3.0 models, sigmoid_sensitivity other than 1.0 and apply_softmax=True raise a ValueError for Raven exports (#111).

• The acoustic 3.0 tf and pb backends, official and custom, reject batch_size above 1 with a ValueError when the session is created: their exports have a static batch dimension of 1, and a larger batch used to crash the worker (pb, and tf with the official file) or silently return a single row (tf with a custom export). onnx and pt take any batch size (#111).

• A Raven export’s own labels/label_names.csv is read as rows of id,label, so load_custom(.., is_raven=True) with that file now yields the species names alone; before, every name carried the id column (Card1,Cardinalis cardinalis_Northern Cardinal) (#111).

• classifier_type is still accepted by load_custom but no longer selects anything: the layout of a custom TFLite classifier is read from the file (#111).

Bugfixes

• Custom 2.4 classifiers exported by current BirdNET-Analyzer releases (Keras 3, TensorFlow 2.16 or newer) failed to load with “Failed to detect custom classifier type”, or an append classifier was misdetected and read the wrong tensor. The prediction tensor’s index depends on the head and on the Keras version that exported it; it is now resolved from the file’s serving_default signature, with the old index table kept as the fallback for exports without usable signature outputs, so every classifier that loaded before still loads with the same scores (#111).

• encode(..) on an append-mode custom 2.4 classifier returned wrong embeddings: the TFLite interpreter reuses the memory of the backbone’s pooling tensor for the head, so reading it after inference gave the head’s intermediate values. The embeddings tensor is now read from an interpreter that preserves intermediate tensors, which an encoding session enables on its first batch (predictions pay nothing) (#111).

• The validity check of load_custom and inspect_custom_tflite opened the model in a process pool whose shutdown waits for its worker, so a model that hung while opening held the caller past the 180 s timeout, and the interpreter at exit. They now run in a process of their own that is killed when the timeout passes, started the way the pipeline starts its workers (spawn unless BIRDNET_START_METHOD says otherwise), so a Linux script that calls them without a __main__ guard needs one, as the acoustic models’ predict already requires (#111).

• On Windows, the lock directory that serializes one-time setup across processes (the V3.0 label generation, and the test suite’s classifier exports) could be left behind by its holder: the release gave up when another process was reading the owner file at that moment, and since the recorded pid stayed alive, every waiter then timed out on a lock nobody held. The release now retries, and as a last resort blanks the pid so waiters adopt the lock after the grace period (#111).

• pip install birdnet[tf] on Intel macOS could resolve to a set of packages that does not import: pyarrow 26.0.0 requires NumPy 2, while TensorFlow 2.16, the newest with wheels there, holds NumPy below 2; and a resolver that picks NumPy 2 first falls back to TensorFlow 2.14, whose binaries cannot load under it. The tf extra now pins TensorFlow 2.16 and pyarrow below 26 on that platform (#111).

Known limitations

• The official acoustic 3.0 SavedModel (pb) and ONNX exports return NaN scores and embeddings for a segment with no variance, such as digital silence or a constant offset; the tf and pt exports of the same model return finite values. Of the custom 3.0 exports, the Raven SavedModel inherits this from its base, while the TFLite export returns finite values. The export, not birdnet, is the place to fix it.

1.1.1 - 2026-09-02

Bugfixes

• A worker killed while writing a large result no longer freezes the whole run. Above 16 KB a queue message crosses the pipe in more than one write, so a killed writer can leave a message whose remainder never arrives — and reading it blocks with nothing raised, before any of the pipeline’s failure handling gets a chance to run. Reachable with top_k=None, with encode at batch_size >= 4, and for large-batch GPU work; library defaults were not affected. The parent now reads every child-to-parent queue through a sacrificial thread with real timeouts, so a half-written message costs a parked thread instead of the session, and the existing liveness check reports the dead worker (#83, #104).

• A child killed on its way out — after signalling it had finished but before its last message was flushed — left the parent waiting on that message forever, invisible to the liveness check because the finish signal was already set. This covered a worker’s end-of-work sentinel, a producer’s unprocessable-input report and per-file completion markers, and the performance tracker’s summary; all of those waits now carry a deadline and fail the run with a clear error naming what was lost (#104).

• Oversized log records (exception dumps with stack traces, exactly what a dying child emits) are truncated before crossing the process boundary, narrowing the widest window for a killed child to leave the session log’s reader a half-written record (#104).

• The official v3.0 model downloads (acoustic and geo, tf/onnx/pt) could serve a stale file: the cache was judged current by byte size alone, which cannot tell two releases apart — a retrained model of the same architecture keeps its size — and let two installed versions sharing one app data directory overwrite each other’s ~0.5 GB downloads on every load. Like the label and taxonomy downloads since 1.1.0, they are now checksum-verified and cached under content-addressed names, so replacing a model in a release forces exactly one re-download; a file cached before this release is renamed to its new name after a one-time hash check, which costs an older installed version sharing the directory one re-download (#105).

1.1.0 - 2026-08-21

Breaking changes

• TensorFlow is now an optional dependency (pip install birdnet[tf]; and-cuda implies it, repro still pins it, onnx stays a no-op alias). The base package ships ONNX Runtime and LiteRT instead, so pip install birdnet runs the 3.0 models via onnx and the 2.4 models via birdnet.load(.., "tf", library="litert"), and is ~1.5 GB smaller. birdnet.load("acoustic", "2.4", "tf"), the pb backend and Perch V2 now raise a ValueError pointing at birdnet[tf]; install that extra to keep the previous behavior. Where LiteRT has no wheels (macOS x86_64, Windows ARM64) the base install cannot run the 2.4 models at all; see the release notes for GPU inference with onnxruntime-gpu (#94).

Added

• Added birdnet.set_download_progress_callback(cb) and a scoped birdnet.download_progress_callback(cb) context manager, so an application can render its own progress for first-run downloads instead of a tqdm bar nobody sees. The callback receives started, throttled progress, retrying and one terminal finished/failed; raising from it cancels the download (#89, #90).

Bugfixes

• TensorFlow’s startup banner is no longer printed by every worker process; it comes from native code, which the previous logging settings could not reach. Failed-import diagnostics are still shown, and BIRDNET_TF_VERBOSE=1 restores the old output (#98).

• V3.0 species names could be silently wrong when two installed versions shared one app data directory, because each judged the other’s label and taxonomy downloads stale by byte size and overwrote them. Those downloads are now checksum-verified and cached per release, and the generated label files record what was actually read, so they regenerate once on upgrade (#99).

• Label and taxonomy setup interrupted by a killed process left a lock directory behind, after which every later load waited out a 300 s timeout and failed until it was removed by hand; the lock now records its owner and is reclaimed when that process is gone. The acoustic V3.0 label setup is serialized across processes as well now, as the geo V3.0 one already was (#99).

• A cached SavedModel from an older release was never recognised as stale, because the pb downloaders only checked that the files were present. Each download now records its source URL and re-downloads on a mismatch, so pb and Perch V2 models cached before this release are fetched once more (#95).

• The repro extra’s TensorFlow pin never applied on Windows, because its marker spelled the platform windows/amd64 instead of win32/AMD64 (#94).

• birdnet.load(.., "tf", library="litert") no longer fails without TensorFlow: the loader rejected every tf load although the LiteRT interpreter never imports TensorFlow. It is now required only for the default tflite interpreter and for pb, so the 2.4 models and custom 2.4 classifiers run in a TensorFlow-free install (#91).

• Acoustic V3.0 confidences were sigmoid-squashed twice — the exports already apply it in-graph — which compressed every score into 0.5–0.73. predict(..) now returns the model’s probabilities unchanged, and sigmoid_sensitivity other than 1.0 or apply_softmax=True raise a ValueError for V3.0, which need logits the exports do not expose (#86).

• The acoustic V3.0 pb backend requested v2.4’s SavedModel signatures, so every predict(..)/encode(..) died with KeyError: 'basic'; it now reads the export’s serving_default signature (#86).

• A worker killed while processing a batch — the realistic out-of-memory case — no longer wedges the surviving workers: waiters on the ring-buffer lock it leaves held now notice the cancelled run and shut down, so the death is reported instead of hanging the call (#73, #82).

• Teardown after a cancelled run no longer hangs on a message a killed child never finished writing: the parent drains the child-to-parent queues on background threads and joins its queue-reader threads with a bound (#77, #82).

• A child killed while writing to the shared logging queue no longer keeps the surviving children from exiting, at the cost of the few records still buffered (#82).

• The progress display’s slot and busy-worker gauges are read without taking the counter’s lock, so a killed process cannot block the stats interval; a reading may be one count behind (#82).

• birdnet.load_perch_v2() required the device argument at runtime although the type stub declared it optional; it now defaults to "CPU" as documented (#94).

• Still open: the consumer’s own read of the results queue can block on a message truncated by a killed worker (#83).

1.0.0 - 2026-08-14

Added

• Added an on_file_complete callback to acoustic predict(..)/encode(..) and their session variants, fired once per file with a single-file result, for streaming per-file persistence and live output. File inputs only, and off the inference hot path, so throughput is unaffected (#57).

• Added the BirdNET V3.0 (preview) acoustic model in the tf, pb, pt and onnx backends, all supporting predict(..) and encode(..). Load via birdnet.load("acoustic", "3.0", <backend>); pt/onnx need the new birdnet[pt]/birdnet[onnx] extras (#41).

• Added the BirdNET-Geomodel V3.0 (v3.0.4, 14,082 classes covering birds, insects, amphibians and mammals) in the same four backends, via birdnet.load("geo", "3.0", <backend>). The pt backend applies the sigmoid the TorchScript export omits, so all four return the same probabilities (#41).

• Added an apply_softmax option to acoustic predict(..), mirroring apply_sigmoid: scores become a softmax over the model logits, useful for confidence scores (e.g. Perch V2). Defaults to False (#54).

• Added partial Python 3.14 support: TensorFlow has no 3.14 wheels, so birdnet installs without it there and runs acoustic 3.0 and geo 3.0 via onnx/pt, while TensorFlow-only paths raise a clear error instead of an ImportError (#55).

Changed

• The inference pipeline now creates its processes with spawn on all platforms instead of inheriting Linux’s fork, which could deadlock workers after TensorFlow had started its multi-threaded runtime. A globally fixed start method is honored and BIRDNET_START_METHOD overrides both, so fork/forkserver stay available by opt-in (#63).

• The V3.0 models now share the geomodel’s versioned taxonomy (taxonomy_v0.2-Jun2026.csv), which resolves every geo label. Estonian (et) was dropped from the V3.0 languages, as the new taxonomy has no Estonian column (#41).

• The progress callback now runs on a background thread with a copy of the caller’s context (contextvars), matching the new on_file_complete callback (#53).

Bugfixes

• A pipeline process that dies mid-run — typically an operating-system kill when memory runs out — is now reported with its name and exit code instead of hanging the call forever. Not covered: a worker killed while processing a batch still deadlocks the survivors on Linux and macOS (#73).

• Fixed the progress callback’s closing update, which reported zero processed segments for completed runs and published nothing at all for a run without predictions, leaving the call waiting indefinitely (#75).

• Model, label and taxonomy downloads now retry with a growing back-off instead of failing on the first transient network fault; permanent client errors still fail immediately.

• Removed a fixed ~1 s barrier from every run_arrays(..) call — on a warm session a 3 s clip went from 1069 ms to 39 ms. Cancelled runs still tear down on the poll interval.

• Fixed an assertion firing in the prediction and encoding workers when the ring-buffer scan finds no readable slot, which aborted the run instead of taking the clean exit already there.

• Producers and the performance tracker no longer attach the ring buffers from inside a fork child, where SharedMemory(create=False) could block forever on a lock CPython does not reinitialize after fork.

• Fixed corrupt rows in acoustic prediction/encoding output caused by growing the internal result buffer with numpy.ndarray.resize, plus an off-by-one in the initial segment count (#50).

• Geo model v3.0 caches now self-heal across releases: a cached SavedModel or label files from an older release were not detected as stale, so a version bump could keep serving outdated labels (#41).

• V3.0 label files now record which taxonomy they were generated from and regenerate when it changes; the taxonomy is shared, so the first model to fetch a new one made it look current for every other model (#41).

0.2.16 - 2026-05-09

Added

• Add support for overriding BirdNET’s application-data directory via an environment variable BIRDNET_APP_DATA, enabling users to place downloaded models/benchmarks in a custom location (useful for deployments with restricted home directories or shared storage).

Bugfixes

• Fixed acoustic inference session being aborted on macOS when stats were enabled: hardened parent/child memory tracking against psutil.AccessDenied, and replaced the two tracked semaphores with a wrapper that mirrors the count into shared memory so get_value() works on macOS (#39)

• Fixed float16 quantization of segment timestamps in prediction results, which caused up to ±0.05 s drift in CSV/DataFrame/Parquet output (#38, #42). Also closed an analogous hole in encoding results where a hop duration that is exactly representable in float16 (e.g. hop=1.5) could still produce drifting accumulated timestamps. Timestamps are now always materialized at >= float32 precision at the source.

0.2.15 - 2026-05-02

Bugfixes

• Fix issue with float16 input durations and hop duration not being exactly representable, which caused rounding errors to accumulate across segments and thus wrong segment times in the output (#32)

0.2.14 - 2026-04-30

Bugfixes

• Allow classifiers trained with hidden units and with append mode (#33, #22)

0.2.13 - 2026-04-06

Changed

• Changed sigmoid function to match birdnet_analyzer by @Josef-Haupt

Bugfixes

• Fixed issue #29

0.2.12 - 2026-02-22

Added

• Added convenience functions to export embeddings by @Josef-Haupt

• Added some code documentation

Changed

• Updated flat sigmoid to match birdnet_analyzer by @Josef-Haupt

Bugfixes

• Fixed issue with one test on Python 3.11 & 3.12

• Fixed issue with building package in tox environments

0.2.11 - 2025-12-09

Added

• Added model metadata to output

• Added more classes to __init__.py for easier imports

• Added skipping of unprocessable inputs

Fixed

• Loading multiple GPUs in parallel processes was not possible

• Fixed hanging problem after error in processing occurred

Changed

• Renamed many of the classes and functions for better clarity

0.2.10 - 2025-11-28

Added

• Added support for Perch model v2

Fixed

• Removed support for LiteRT on macOS ARM64 due to incompatibility issues

0.2.9 - 2025-11-27

Added

• Added option to supervise progress using callback function during inference

0.2.8 - 2025-11-26

Added

• Added option to predict and encode raw audio numpy arrays

0.2.7 - 2025-11-25

Added

• Added parameter speed to control playback speed of audio during inference

0.2.5 - 2025-11-17 & 0.2.6 - 2025-11-21

Bugfix

• Fixed issue with using ProtoBuf CPU backend and TensorFlow GPU being available

• Fixed #17: Issue on macOS with too long ring buffer names

• Fixed #19: queue.qsize() is not used anymore

• Fixed issue with hanging session because of logging

• Fixed issue with downloading same model simultaneously

Changed

• Removed litert install option, now litert is always installed if possible

• Rename tf library to tflite to better reflect the usage of TFLite/LiteRT

• Improved prediction speed, esp. for half-precision models (+10 seg/s)

• Lowered dependencies

• Update ai-edge-litert to version 2.0.3 on repro

• Better download progress indication of model files

• Model loading in tests is done before running other tests

Added

• Added repro option to be able to get reproducible results

• Added support for Python 3.13

• Added CI on GitHub Actions for testing on multiple OS and Python versions

Removed

• Remove unused dependencies numba and resampy

0.2.4 - 2025-11-05

Bugfixes

• Fixed issue with loading supported files from a folder

Changed

• Increased half-precision prediction speed

• Set “pyarrow==22.0.0”

• Set “numpy==2.0.2” because of compatibility with perch-hoplite

• Set default “half_precision” parameter to False because of lower speed

Added

• Add half precision to CLI

0.2.3 - 2025-11-04

Added

• Added support for Python 3.12

Changed

• Changed tensorflow to newest version 2.20.0

0.2.2 - 2025-11-03

Added

• Added support for running multiple sessions in a row or in parallel using threading or multiprocessing

• Each session has its own logger and log file

Changed

• Changed naming of the benchmark output files

0.2.1 - 2025-10-29

Added

• Added parameter is_raven to load function to specify whether a custom Protobuf model is a Raven model or not

• Fix int8 acoustic model wrong inference parameters

• Added tests

0.2.0 - 2025-10-27

Changed

• Refactored the whole codebase to be able to load model and predict scores in two separate steps

0.2.0a0 - 2025-07-29

Changed

• Refactored the whole codebase

0.1.7 - 2025-03-19

Changed

• Switched model download links from TUCcloud to Zenodo #10

Fixed

• Added check for mono files #9

0.1.6 - 2024-09-04

Added

• Support for multiprocessing using predict_species_within_audio_files_mp

Changed

• Separate ModelV2M4TFLite into AudioModelV2M4TFLite and MetaModelV2M4TFLite

• Separate ModelV2M4Protobuf into AudioModelV2M4Protobuf and MetaModelV2M4Protobuf

• Separate ModelV2M4 into AudioModelV2M4 and MetaModelV2M4

• Move v2.4 models to birdnet.models.v2m4

• Yield results of predict_species_within_audio_file instead of returning an OrderedDict

• Extracted method predict_species_within_audio_file and predict_species_at_location_and_time from their respective model

• set default value for batch_size to 100

0.1.5 - 2024-08-16

Fixed

• Custom Raven audio model didn’t return same results as custom TFLite model because of sigmoid layer

• TFLite meta model was not returning correct results

Changed

• Rename CustomModelV2M4TFLite to CustomAudioModelV2M4TFLite

• Rename CustomModelV2M4Raven to CustomAudioModelV2M4Raven

0.1.4 - 2024-08-13

Added

• Support to load custom TFLite models using CustomModelV2M4TFLite

• Support to load custom Raven (Protobuf) models using CustomModelV2M4Raven

0.1.3 - 2024-08-13

Changed

• Make CUDA dependency optional, install with birdnet[and-cuda]

Fixed

• Bugfix ‘ERROR: Could not find a version that satisfies the requirement nvidia-cuda-nvcc-cu12 (Mac/Ubuntu/Windows)’ (#4)

0.1.2 - 2024-08-07

Added

• Add GPU support by introducing the Protobuf model (v2.4)

Changed

• Rename class ‘ModelV2M4’ to ‘ModelV2M4TFLite’

• ‘ModelV2M4’ defaults to Protobuf model now

• Sorting of prediction scores is now: score (desc) & name (asc)

Fixed

• Bugfix output interval durations are now always of type ‘float’

0.1.1 - 2024-08-02

Added

• Add parameter ‘chunk_overlap_s’ to define overlapping between chunks (#3)

Removed

• Remove parameter ‘file_splitting_duration_s’ instead load files in 3s chunks (#2)

• Remove ‘librosa’ dependency

0.1.0 - 2024-07-23

• Initial release