Merge remote-tracking branch 'upstream/master' into prompt

* fix word timestamps for batched inference

* remove hf pipeline

MANIFEST.in

@@ -1,3 +1,4 @@
 include faster_whisper/assets/silero_vad.onnx
 include requirements.txt
 include requirements.conversion.txt
+include faster_whisper/assets/pyannote_vad_model.bin

README.md

@@ -69,34 +69,40 @@ segments, info = model.transcribe("audio.mp3", beam_size=5, language="en")
 
 * Python 3.8 or greater
 
-Unlike openai-whisper, FFmpeg does **not** need to be installed on the system. The audio is decoded with the Python library [PyAV](https://github.com/PyAV-Org/PyAV) which bundles the FFmpeg libraries in its package.
 
 ### GPU
 
 GPU execution requires the following NVIDIA libraries to be installed:
 
-* [cuBLAS for CUDA 11](https://developer.nvidia.com/cublas)
-* [cuDNN 8 for CUDA 11](https://developer.nvidia.com/cudnn)
+* [cuBLAS for CUDA 12](https://developer.nvidia.com/cublas)
+* [cuDNN 8 for CUDA 12](https://developer.nvidia.com/cudnn)
 
-There are multiple ways to install these libraries. The recommended way is described in the official NVIDIA documentation, but we also suggest other installation methods below.
+**Note**: Latest versions of `ctranslate2` support CUDA 12 only. For CUDA 11, the current workaround is downgrading to the `3.24.0` version of `ctranslate2` (This can be done with `pip install --force-reinstall ctranslate2==3.24.0` or specifying the version in a `requirements.txt`).
+
+There are multiple ways to install the NVIDIA libraries mentioned above. The recommended way is described in the official NVIDIA documentation, but we also suggest other installation methods below. 
 
 <details>
 <summary>Other installation methods (click to expand)</summary>
 
+
+**Note:** For all these methods below, keep in mind the above note regarding CUDA versions. Depending on your setup, you may need to install the _CUDA 11_ versions of libraries that correspond to the CUDA 12 libraries listed in the instructions below.
+
 #### Use Docker
 
-The libraries are installed in this official NVIDIA Docker image: `nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu22.04`.
+The libraries (cuBLAS, cuDNN) are installed in these official NVIDIA CUDA Docker images: `nvidia/cuda:12.0.0-runtime-ubuntu20.04` or `nvidia/cuda:12.0.0-runtime-ubuntu22.04`.
 
 #### Install with `pip` (Linux only)
 
 On Linux these libraries can be installed with `pip`. Note that `LD_LIBRARY_PATH` must be set before launching Python.
 
 ```bash
-pip install nvidia-cublas-cu11 nvidia-cudnn-cu11
+pip install nvidia-cublas-cu12 nvidia-cudnn-cu12
 
 export LD_LIBRARY_PATH=`python3 -c 'import os; import nvidia.cublas.lib; import nvidia.cudnn.lib; print(os.path.dirname(nvidia.cublas.lib.__file__) + ":" + os.path.dirname(nvidia.cudnn.lib.__file__))'`
 ```
 
+**Note**: Version 9+ of `nvidia-cudnn-cu12` appears to cause issues due its reliance on cuDNN 9 (Faster-Whisper does not currently support cuDNN 9). Ensure your version of the Python package is for cuDNN 8.
+
 #### Download the libraries from Purfview's repository (Windows & Linux)
 
 Purfview's [whisper-standalone-win](https://github.com/Purfview/whisper-standalone-win) provides the required NVIDIA libraries for Windows & Linux in a [single archive](https://github.com/Purfview/whisper-standalone-win/releases/tag/libs). Decompress the archive and place the libraries in a directory included in the `PATH`.
@@ -159,6 +165,35 @@ for segment in segments:
 segments, _ = model.transcribe("audio.mp3")
 segments = list(segments)  # The transcription will actually run here.
 ```
+
+### multi-segment language detection
+
+To directly use the model for improved language detection, the following code snippet can be used:
+
+```python
+from faster_whisper import WhisperModel
+model = WhisperModel("medium", device="cuda", compute_type="float16")
+language_info = model.detect_language_multi_segment("audio.mp3")
+```
+
+### Batched faster-whisper
+
+
+The batched version of faster-whisper is inspired by [whisper-x](https://github.com/m-bain/whisperX) licensed under the BSD-2 Clause license and integrates its VAD model to this library. We modify this implementation and also replaced the feature extraction with a faster torch-based implementation. Batched version improves the speed upto 10-12x compared to openAI implementation and 3-4x compared to the sequential faster_whisper version. It works by transcribing semantically meaningful audio chunks as batches leading to faster inference. 
+
+The following code snippet illustrates how to run inference with batched version on an example audio file. Please also refer to the test scripts of batched faster whisper.
+
+```python
+from faster_whisper import WhisperModel, BatchedInferencePipeline
+
+model = WhisperModel("medium", device="cuda", compute_type="float16")
+batched_model = BatchedInferencePipeline(model=model)
+segments, info = batched_model.transcribe("audio.mp3", batch_size=16)
+
+for segment in segments:
+    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
+```
+
 ### Faster Distil-Whisper
 
 The Distil-Whisper checkpoints are compatible with the Faster-Whisper package. In particular, the latest [distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3)
@@ -227,6 +262,7 @@ See more model and transcription options in the [`WhisperModel`](https://github.
 Here is a non exhaustive list of open-source projects using faster-whisper. Feel free to add your project to the list!
 
 
+* [faster-whisper-server](https://github.com/fedirz/faster-whisper-server) is an OpenAI compatible server using `faster-whisper`. It's easily deployable with Docker, works with OpenAI SDKs/CLI, supports streaming, and live transcription.
 * [WhisperX](https://github.com/m-bain/whisperX) is an award-winning Python library that offers speaker diarization and accurate word-level timestamps using wav2vec2 alignment
 * [whisper-ctranslate2](https://github.com/Softcatala/whisper-ctranslate2) is a command line client based on faster-whisper and compatible with the original client from openai/whisper.
 * [whisper-diarize](https://github.com/MahmoudAshraf97/whisper-diarization) is a speaker diarization tool that is based on faster-whisper and NVIDIA NeMo.

benchmark/memory_benchmark.py

@@ -0,0 +1,94 @@
+import argparse
+import time
+
+from typing import Callable
+
+import py3nvml.py3nvml as nvml
+
+from memory_profiler import memory_usage
+from utils import MyThread, get_logger, inference
+
+logger = get_logger("faster-whisper")
+parser = argparse.ArgumentParser(description="Memory benchmark")
+parser.add_argument(
+    "--gpu_memory", action="store_true", help="Measure GPU memory usage"
+)
+parser.add_argument("--device-index", type=int, default=0, help="GPU device index")
+parser.add_argument(
+    "--interval",
+    type=float,
+    default=0.5,
+    help="Interval at which measurements are collected",
+)
+args = parser.parse_args()
+device_idx = args.device_index
+interval = args.interval
+
+
+def measure_memory(func: Callable[[], None]):
+    if args.gpu_memory:
+        logger.info(
+            "Measuring maximum GPU memory usage on GPU device."
+            " Make sure to not have additional processes running on the same GPU."
+        )
+        # init nvml
+        nvml.nvmlInit()
+        handle = nvml.nvmlDeviceGetHandleByIndex(device_idx)
+        gpu_name = nvml.nvmlDeviceGetName(handle)
+        gpu_memory_limit = nvml.nvmlDeviceGetMemoryInfo(handle).total >> 20
+        gpu_power_limit = nvml.nvmlDeviceGetPowerManagementLimit(handle) / 1000.0
+        info = {"gpu_memory_usage": [], "gpu_power_usage": []}
+
+        def _get_gpu_info():
+            while True:
+                info["gpu_memory_usage"].append(
+                    nvml.nvmlDeviceGetMemoryInfo(handle).used >> 20
+                )
+                info["gpu_power_usage"].append(
+                    nvml.nvmlDeviceGetPowerUsage(handle) / 1000
+                )
+                time.sleep(interval)
+
+                if stop:
+                    break
+
+            return info
+
+        stop = False
+        thread = MyThread(_get_gpu_info, params=())
+        thread.start()
+        func()
+        stop = True
+        thread.join()
+        result = thread.get_result()
+
+        # shutdown nvml
+        nvml.nvmlShutdown()
+        max_memory_usage = max(result["gpu_memory_usage"])
+        max_power_usage = max(result["gpu_power_usage"])
+        print("GPU name: %s" % gpu_name)
+        print("GPU device index: %s" % device_idx)
+        print(
+            "Maximum GPU memory usage: %dMiB / %dMiB (%.2f%%)"
+            % (
+                max_memory_usage,
+                gpu_memory_limit,
+                (max_memory_usage / gpu_memory_limit) * 100,
+            )
+        )
+        print(
+            "Maximum GPU power usage: %dW / %dW (%.2f%%)"
+            % (
+                max_power_usage,
+                gpu_power_limit,
+                (max_power_usage / gpu_power_limit) * 100,
+            )
+        )
+    else:
+        logger.info("Measuring maximum increase of memory usage.")
+        max_usage = memory_usage(func, max_usage=True, interval=interval)
+        print("Maximum increase of RAM memory usage: %d MiB" % max_usage)
+
+
+if __name__ == "__main__":
+    measure_memory(inference)

benchmark/requirements.benchmark.txt

@@ -0,0 +1,6 @@
+transformers
+jiwer
+evaluate
+datasets
+memory_profiler
+py3nvml

benchmark/speed_benchmark.py

@@ -0,0 +1,31 @@
+import argparse
+import timeit
+
+from typing import Callable
+
+from utils import inference
+
+parser = argparse.ArgumentParser(description="Speed benchmark")
+parser.add_argument(
+    "--repeat",
+    type=int,
+    default=3,
+    help="Times an experiment will be run.",
+)
+args = parser.parse_args()
+
+
+def measure_speed(func: Callable[[], None]):
+    # as written in https://docs.python.org/3/library/timeit.html#timeit.Timer.repeat,
+    # min should be taken rather than the average
+    runtimes = timeit.repeat(
+        func,
+        repeat=args.repeat,
+        number=10,
+    )
+    print(runtimes)
+    print("Min execution time: %.3fs" % (min(runtimes) / 10.0))
+
+
+if __name__ == "__main__":
+    measure_speed(inference)

benchmark/utils.py

@@ -0,0 +1,39 @@
+import logging
+
+from threading import Thread
+from typing import Optional
+
+from faster_whisper import WhisperModel
+
+model_path = "large-v3"
+model = WhisperModel(model_path, device="cuda")
+
+
+def inference():
+    segments, info = model.transcribe("benchmark.m4a", language="fr")
+    for segment in segments:
+        print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
+
+
+def get_logger(name: Optional[str] = None) -> logging.Logger:
+    formatter = logging.Formatter("%(levelname)s: %(message)s")
+    logger = logging.getLogger(name)
+    logger.setLevel(logging.DEBUG)
+    handler = logging.StreamHandler()
+    handler.setFormatter(formatter)
+    logger.addHandler(handler)
+    return logger
+
+
+class MyThread(Thread):
+    def __init__(self, func, params):
+        super(MyThread, self).__init__()
+        self.func = func
+        self.params = params
+        self.result = None
+
+    def run(self):
+        self.result = self.func(*self.params)
+
+    def get_result(self):
+        return self.result

benchmark/wer_benchmark.py

@@ -0,0 +1,64 @@
+import argparse
+import json
+import os
+
+from datasets import load_dataset
+from evaluate import load
+from tqdm import tqdm
+from transformers.models.whisper.english_normalizer import EnglishTextNormalizer
+
+from faster_whisper import WhisperModel
+
+parser = argparse.ArgumentParser(description="WER benchmark")
+parser.add_argument(
+    "--audio_numb",
+    type=int,
+    default=None,
+    help="Specify the number of validation audio files in the dataset."
+    " Set to None to retrieve all audio files.",
+)
+args = parser.parse_args()
+
+model_path = "large-v3"
+model = WhisperModel(model_path, device="cuda")
+
+# load the dataset with streaming mode
+dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
+
+# define the evaluation metric
+wer_metric = load("wer")
+
+with open(os.path.join(os.path.dirname(__file__), "normalizer.json"), "r") as f:
+    normalizer = EnglishTextNormalizer(json.load(f))
+
+
+def inference(batch):
+    batch["transcription"] = []
+    for sample in batch["audio"]:
+        segments, info = model.transcribe(sample["array"], language="en")
+        batch["transcription"].append("".join([segment.text for segment in segments]))
+    batch["reference"] = batch["text"]
+    return batch
+
+
+dataset = dataset.map(function=inference, batched=True, batch_size=16)
+
+all_transcriptions = []
+all_references = []
+
+# iterate over the dataset and run inference
+for i, result in tqdm(enumerate(dataset), desc="Evaluating..."):
+    all_transcriptions.append(result["transcription"])
+    all_references.append(result["reference"])
+    if args.audio_numb and i == (args.audio_numb - 1):
+        break
+
+# normalize predictions and references
+all_transcriptions = [normalizer(transcription) for transcription in all_transcriptions]
+all_references = [normalizer(reference) for reference in all_references]
+
+# compute the WER metric
+wer = 100 * wer_metric.compute(
+    predictions=all_transcriptions, references=all_references
+)
+print("WER: %.3f" % wer)

docker/Dockerfile

@@ -0,0 +1,6 @@
+FROM nvidia/cuda:12.2.2-cudnn8-runtime-ubuntu22.04
+WORKDIR /root
+RUN apt-get update -y && apt-get install -y python3-pip
+COPY infer.py jfk.flac ./
+RUN pip3 install faster-whisper
+CMD ["python3", "infer.py"]

docker/infer.py

@@ -0,0 +1,7 @@
+from faster_whisper import WhisperModel
+
+jfk_path = "jfk.flac"
+model = WhisperModel("tiny", device="cuda")
+segments, info = model.transcribe(jfk_path, word_timestamps=True)
+for segment in segments:
+    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))

faster_whisper/__init__.py

@@ -1,5 +1,5 @@
 from faster_whisper.audio import decode_audio
-from faster_whisper.transcribe import WhisperModel
+from faster_whisper.transcribe import BatchedInferencePipeline, WhisperModel
 from faster_whisper.utils import available_models, download_model, format_timestamp
 from faster_whisper.version import __version__
 
@@ -7,6 +7,7 @@ __all__ = [
     "available_models",
     "decode_audio",
     "WhisperModel",
+    "BatchedInferencePipeline",
     "download_model",
     "format_timestamp",
     "__version__",

faster_whisper/audio.py

@@ -1,19 +1,7 @@
-"""We use the PyAV library to decode the audio: https://github.com/PyAV-Org/PyAV
-
-The advantage of PyAV is that it bundles the FFmpeg libraries so there is no additional
-system dependencies. FFmpeg does not need to be installed on the system.
-
-However, the API is quite low-level so we need to manipulate audio frames directly.
-"""
-
-import gc
-import io
-import itertools
-
 from typing import BinaryIO, Union
 
-import av
-import numpy as np
+import torch
+import torchaudio
 
 
 def decode_audio(
@@ -29,91 +17,42 @@ def decode_audio(
       split_stereo: Return separate left and right channels.
 
     Returns:
-      A float32 Numpy array.
+      A float32 Torch Tensor.
 
       If `split_stereo` is enabled, the function returns a 2-tuple with the
       separated left and right channels.
     """
-    resampler = av.audio.resampler.AudioResampler(
-        format="s16",
-        layout="mono" if not split_stereo else "stereo",
-        rate=sampling_rate,
-    )
 
-    raw_buffer = io.BytesIO()
-    dtype = None
-
-    with av.open(input_file, mode="r", metadata_errors="ignore") as container:
-        frames = container.decode(audio=0)
-        frames = _ignore_invalid_frames(frames)
-        frames = _group_frames(frames, 500000)
-        frames = _resample_frames(frames, resampler)
-
-        for frame in frames:
-            array = frame.to_ndarray()
-            dtype = array.dtype
-            raw_buffer.write(array)
-
-    # It appears that some objects related to the resampler are not freed
-    # unless the garbage collector is manually run.
-    del resampler
-    gc.collect()
-
-    audio = np.frombuffer(raw_buffer.getbuffer(), dtype=dtype)
-
-    # Convert s16 back to f32.
-    audio = audio.astype(np.float32) / 32768.0
+    waveform, audio_sf = torchaudio.load(input_file)  # waveform: channels X T
 
+    if audio_sf != sampling_rate:
+        waveform = torchaudio.functional.resample(
+            waveform, orig_freq=audio_sf, new_freq=sampling_rate
+        )
     if split_stereo:
-        left_channel = audio[0::2]
-        right_channel = audio[1::2]
-        return left_channel, right_channel
+        return waveform[0], waveform[1]
 
-    return audio
-
-
-def _ignore_invalid_frames(frames):
-    iterator = iter(frames)
-
-    while True:
-        try:
-            yield next(iterator)
-        except StopIteration:
-            break
-        except av.error.InvalidDataError:
-            continue
-
-
-def _group_frames(frames, num_samples=None):
-    fifo = av.audio.fifo.AudioFifo()
-
-    for frame in frames:
-        frame.pts = None  # Ignore timestamp check.
-        fifo.write(frame)
-
-        if num_samples is not None and fifo.samples >= num_samples:
-            yield fifo.read()
-
-    if fifo.samples > 0:
-        yield fifo.read()
-
-
-def _resample_frames(frames, resampler):
-    # Add None to flush the resampler.
-    for frame in itertools.chain(frames, [None]):
-        yield from resampler.resample(frame)
+    return waveform.mean(0)
 
 
 def pad_or_trim(array, length: int, *, axis: int = -1):
     """
     Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
     """
+    axis = axis % array.ndim
     if array.shape[axis] > length:
-        array = array.take(indices=range(length), axis=axis)
+        idx = [Ellipsis] * axis + [slice(length)] + [Ellipsis] * (array.ndim - axis - 1)
+        return array[idx]
 
     if array.shape[axis] < length:
-        pad_widths = [(0, 0)] * array.ndim
-        pad_widths[axis] = (0, length - array.shape[axis])
-        array = np.pad(array, pad_widths)
+        pad_widths = (
+            [
+                0,
+            ]
+            * array.ndim
+            * 2
+        )
+        pad_widths[2 * axis] = length - array.shape[axis]
+        array = torch.nn.functional.pad(array, tuple(pad_widths[::-1]))
 
     return array

faster_whisper/feature_extractor.py

@@ -1,16 +1,21 @@
-import numpy as np
+import torch
 
 
 # Adapted from https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/feature_extraction_whisper.py  # noqa: E501
 class FeatureExtractor:
     def __init__(
         self,
+        device: str = "auto",
         feature_size=80,
         sampling_rate=16000,
         hop_length=160,
         chunk_length=30,
         n_fft=400,
     ):
+        if device == "auto":
+            self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        else:
+            self.device = device
         self.n_fft = n_fft
         self.hop_length = hop_length
         self.chunk_length = chunk_length
@@ -22,21 +27,22 @@ class FeatureExtractor:
             sampling_rate, n_fft, n_mels=feature_size
         )
 
-    def get_mel_filters(self, sr, n_fft, n_mels=128, dtype=np.float32):
+    @staticmethod
+    def get_mel_filters(sr, n_fft, n_mels=128):
+        """
+        Implementation of librosa.filters.mel in Pytorch
+        """
         # Initialize the weights
         n_mels = int(n_mels)
-        weights = np.zeros((n_mels, int(1 + n_fft // 2)), dtype=dtype)
 
         # Center freqs of each FFT bin
-        fftfreqs = np.fft.rfftfreq(n=n_fft, d=1.0 / sr)
+        fftfreqs = torch.fft.rfftfreq(n=n_fft, d=1.0 / sr)
 
         # 'Center freqs' of mel bands - uniformly spaced between limits
         min_mel = 0.0
         max_mel = 45.245640471924965
 
-        mels = np.linspace(min_mel, max_mel, n_mels + 2)
-
-        mels = np.asanyarray(mels)
+        mels = torch.linspace(min_mel, max_mel, n_mels + 2)
 
         # Fill in the linear scale
         f_min = 0.0
@@ -46,125 +52,63 @@ class FeatureExtractor:
         # And now the nonlinear scale
         min_log_hz = 1000.0  # beginning of log region (Hz)
         min_log_mel = (min_log_hz - f_min) / f_sp  # same (Mels)
-        logstep = np.log(6.4) / 27.0  # step size for log region
+        logstep = torch.log(torch.tensor(6.4)) / 27.0  # step size for log region
 
         # If we have vector data, vectorize
         log_t = mels >= min_log_mel
-        freqs[log_t] = min_log_hz * np.exp(logstep * (mels[log_t] - min_log_mel))
+        freqs[log_t] = min_log_hz * torch.exp(logstep * (mels[log_t] - min_log_mel))
 
         mel_f = freqs
 
-        fdiff = np.diff(mel_f)
-        ramps = np.subtract.outer(mel_f, fftfreqs)
+        fdiff = torch.diff(mel_f)
+        ramps = mel_f.view(-1, 1) - fftfreqs.view(1, -1)
 
-        for i in range(n_mels):
-            # lower and upper slopes for all bins
-            lower = -ramps[i] / fdiff[i]
-            upper = ramps[i + 2] / fdiff[i + 1]
+        lower = -ramps[:-2] / fdiff[:-1].unsqueeze(1)
+        upper = ramps[2:] / fdiff[1:].unsqueeze(1)
 
-            # .. then intersect them with each other and zero
-            weights[i] = np.maximum(0, np.minimum(lower, upper))
+        # Intersect them with each other and zero, vectorized across all i
+        weights = torch.maximum(torch.zeros_like(lower), torch.minimum(lower, upper))
 
         # Slaney-style mel is scaled to be approx constant energy per channel
         enorm = 2.0 / (mel_f[2 : n_mels + 2] - mel_f[:n_mels])
-        weights *= enorm[:, np.newaxis]
+        weights *= enorm.unsqueeze(1)
 
         return weights
 
-    def fram_wave(self, waveform, center=True):
+    def __call__(self, waveform, padding=True, chunk_length=None, to_cpu=False):
         """
-        Transform a raw waveform into a list of smaller waveforms.
-        The window length defines how much of the signal is
-        contain in each frame (smalle waveform), while the hope length defines the step
-        between the beginning of each new frame.
-        Centering is done by reflecting the waveform which is first centered around
-        `frame_idx * hop_length`.
+        Compute the log-Mel spectrogram of the provided audio.
         """
-        frames = []
-        for i in range(0, waveform.shape[0] + 1, self.hop_length):
-            half_window = (self.n_fft - 1) // 2 + 1
-            if center:
-                start = i - half_window if i > half_window else 0
-                end = (
-                    i + half_window
-                    if i < waveform.shape[0] - half_window
-                    else waveform.shape[0]
-                )
 
-                frame = waveform[start:end]
-
-                if start == 0:
-                    padd_width = (-i + half_window, 0)
-                    frame = np.pad(frame, pad_width=padd_width, mode="reflect")
-
-                elif end == waveform.shape[0]:
-                    padd_width = (0, (i - waveform.shape[0] + half_window))
-                    frame = np.pad(frame, pad_width=padd_width, mode="reflect")
-
-            else:
-                frame = waveform[i : i + self.n_fft]
-                frame_width = frame.shape[0]
-                if frame_width < waveform.shape[0]:
-                    frame = np.lib.pad(
-                        frame,
-                        pad_width=(0, self.n_fft - frame_width),
-                        mode="constant",
-                        constant_values=0,
-                    )
-
-            frames.append(frame)
-        return np.stack(frames, 0)
-
-    def stft(self, frames, window):
-        """
-        Calculates the complex Short-Time Fourier Transform (STFT) of the given framed signal.
-        Should give the same results as `torch.stft`.
-        """
-        frame_size = frames.shape[1]
-        fft_size = self.n_fft
-
-        if fft_size is None:
-            fft_size = frame_size
-
-        if fft_size < frame_size:
-            raise ValueError("FFT size must greater or equal the frame size")
-        # number of FFT bins to store
-        num_fft_bins = (fft_size >> 1) + 1
-
-        data = np.empty((len(frames), num_fft_bins), dtype=np.complex64)
-        fft_signal = np.zeros(fft_size)
-
-        for f, frame in enumerate(frames):
-            if window is not None:
-                np.multiply(frame, window, out=fft_signal[:frame_size])
-            else:
-                fft_signal[:frame_size] = frame
-            data[f] = np.fft.fft(fft_signal, axis=0)[:num_fft_bins]
-        return data.T
-
-    def __call__(self, waveform, padding=True, chunk_length=None):
-        """
-        Compute the log-Mel spectrogram of the provided audio, gives similar results
-        whisper's original torch implementation with 1e-5 tolerance.
-        """
         if chunk_length is not None:
             self.n_samples = chunk_length * self.sampling_rate
             self.nb_max_frames = self.n_samples // self.hop_length
 
+        if waveform.dtype is not torch.float32:
+            waveform = waveform.to(torch.float32)
+
+        waveform = (
+            waveform.to(self.device)
+            if self.device == "cuda" and not waveform.is_cuda
+            else waveform
+        )
+
         if padding:
-            waveform = np.pad(waveform, [(0, self.n_samples)])
+            waveform = torch.nn.functional.pad(waveform, (0, self.n_samples))
 
-        window = np.hanning(self.n_fft + 1)[:-1]
+        window = torch.hann_window(self.n_fft).to(waveform.device)
 
-        frames = self.fram_wave(waveform)
-        stft = self.stft(frames, window=window)
-        magnitudes = np.abs(stft[:, :-1]) ** 2
+        stft = torch.stft(
+            waveform, self.n_fft, self.hop_length, window=window, return_complex=True
+        )
+        magnitudes = stft[..., :-1].abs() ** 2
 
-        filters = self.mel_filters
-        mel_spec = filters @ magnitudes
+        mel_spec = self.mel_filters.to(waveform.device) @ magnitudes
 
-        log_spec = np.log10(np.clip(mel_spec, a_min=1e-10, a_max=None))
-        log_spec = np.maximum(log_spec, log_spec.max() - 8.0)
+        log_spec = torch.clamp(mel_spec, min=1e-10).log10()
+        log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
         log_spec = (log_spec + 4.0) / 4.0
 
-        return log_spec
+        # When the model is running on multiple GPUs, the output should be moved
+        # to the CPU since we don't know which GPU will handle the next job.
+        return log_spec.cpu() if to_cpu else log_spec

faster_whisper/tokenizer.py

@@ -105,6 +105,42 @@ class Tokenizer:
             [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs]
         )
 
+    @cached_property
+    def non_speech_tokens(self) -> Tuple[int]:
+        """
+        Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech
+        annotations, to prevent sampling texts that are not actually spoken in the audio, e.g.
+
+        - ♪♪♪
+        - ( SPEAKING FOREIGN LANGUAGE )
+        - [DAVID] Hey there,
+
+        keeping basic punctuations like commas, periods, question marks, exclamation points, etc.
+        """
+        symbols = list('"#()*+/:;<=>@[\\]^_`{|}~「」『』')
+        symbols += (
+            "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split()
+        )
+
+        # symbols that may be a single token or multiple tokens depending on the tokenizer.
+        # In case they're multiple tokens, suppress the first token, which is safe because:
+        # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress
+        # in generations, and in the 3-byte UTF-8 representation they share the first two bytes.
+        miscellaneous = set("♩♪♫♬♭♮♯")
+        assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous)
+
+        # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word
+        result = {self.encode(" -")[0], self.encode(" '")[0]}
+        for symbol in symbols + list(miscellaneous):
+            for tokens in [
+                self.encode(symbol),
+                self.encode(" " + symbol),
+            ]:
+                if len(tokens) == 1 or symbol in miscellaneous:
+                    result.add(tokens[0])
+
+        return tuple(sorted(result))
+
     def split_to_word_tokens(
         self, tokens: List[int]
     ) -> Tuple[List[str], List[List[int]]]:

faster_whisper/utils.py

@@ -54,8 +54,9 @@ def download_model(
 
     Args:
       size_or_id: Size of the model to download from https://huggingface.co/Systran
-        (tiny, tiny.en, base, base.en, small, small.en medium, medium.en, large-v1, large-v2,
-        large-v3, large), or a CTranslate2-converted model ID from the Hugging Face Hub
+        (tiny, tiny.en, base, base.en, small, small.en, distil-small.en, medium, medium.en,
+        distil-medium.en, large-v1, large-v2, large-v3, large, distil-large-v2,
+        distil-large-v3), or a CTranslate2-converted model ID from the Hugging Face Hub
         (e.g. Systran/faster-whisper-large-v3).
       output_dir: Directory where the model should be saved. If not set, the model is saved in
         the cache directory.

faster_whisper/vad.py

@@ -1,11 +1,18 @@
 import bisect
 import functools
 import os
-import warnings
 
-from typing import List, NamedTuple, Optional
+from abc import ABC
+from collections.abc import Callable
+from typing import List, NamedTuple, Optional, Union
 
 import numpy as np
+import torch
+
+from pyannote.audio.core.io import AudioFile
+from pyannote.audio.pipelines import VoiceActivityDetection
+from pyannote.audio.pipelines.utils import PipelineModel
+from pyannote.core import Annotation, Segment, SlidingWindowFeature
 
 from faster_whisper.utils import get_assets_path
 
@@ -25,9 +32,6 @@ class VadOptions(NamedTuple):
         split aggressively just before max_speech_duration_s.
       min_silence_duration_ms: In the end of each speech chunk wait for min_silence_duration_ms
         before separating it
-      window_size_samples: Audio chunks of window_size_samples size are fed to the silero VAD model.
-        WARNING! Silero VAD models were trained using 512, 1024, 1536 samples for 16000 sample rate.
-        Values other than these may affect model performance!!
       speech_pad_ms: Final speech chunks are padded by speech_pad_ms each side
     """
 
@@ -35,12 +39,11 @@ class VadOptions(NamedTuple):
     min_speech_duration_ms: int = 250
     max_speech_duration_s: float = float("inf")
     min_silence_duration_ms: int = 2000
-    window_size_samples: int = 1024
     speech_pad_ms: int = 400
 
 
 def get_speech_timestamps(
-    audio: np.ndarray,
+    audio: torch.Tensor,
     vad_options: Optional[VadOptions] = None,
     **kwargs,
 ) -> List[dict]:
@@ -61,15 +64,8 @@ def get_speech_timestamps(
     min_speech_duration_ms = vad_options.min_speech_duration_ms
     max_speech_duration_s = vad_options.max_speech_duration_s
     min_silence_duration_ms = vad_options.min_silence_duration_ms
-    window_size_samples = vad_options.window_size_samples
+    window_size_samples = 512
     speech_pad_ms = vad_options.speech_pad_ms
-
-    if window_size_samples not in [512, 1024, 1536]:
-        warnings.warn(
-            "Unusual window_size_samples! Supported window_size_samples:\n"
-            " - [512, 1024, 1536] for 16000 sampling_rate"
-        )
-
     sampling_rate = 16000
     min_speech_samples = sampling_rate * min_speech_duration_ms / 1000
     speech_pad_samples = sampling_rate * speech_pad_ms / 1000
@@ -84,14 +80,14 @@ def get_speech_timestamps(
     audio_length_samples = len(audio)
 
     model = get_vad_model()
-    state = model.get_initial_state(batch_size=1)
+    state, context = model.get_initial_states(batch_size=1)
 
     speech_probs = []
     for current_start_sample in range(0, audio_length_samples, window_size_samples):
         chunk = audio[current_start_sample : current_start_sample + window_size_samples]
         if len(chunk) < window_size_samples:
             chunk = np.pad(chunk, (0, int(window_size_samples - len(chunk))))
-        speech_prob, state = model(chunk, state, sampling_rate)
+        speech_prob, state, context = model(chunk, state, context, sampling_rate)
         speech_probs.append(speech_prob)
 
     triggered = False
@@ -188,12 +184,12 @@ def get_speech_timestamps(
     return speeches
 
 
-def collect_chunks(audio: np.ndarray, chunks: List[dict]) -> np.ndarray:
+def collect_chunks(audio: torch.Tensor, chunks: List[dict]) -> torch.Tensor:
     """Collects and concatenates audio chunks."""
     if not chunks:
-        return np.array([], dtype=np.float32)
+        return torch.tensor([], dtype=torch.float32)
 
-    return np.concatenate([audio[chunk["start"] : chunk["end"]] for chunk in chunks])
+    return torch.cat([audio[chunk["start"] : chunk["end"]] for chunk in chunks])
 
 
 class SpeechTimestampsMap:
@@ -261,12 +257,12 @@ class SileroVADModel:
             sess_options=opts,
         )
 
-    def get_initial_state(self, batch_size: int):
-        h = np.zeros((2, batch_size, 64), dtype=np.float32)
-        c = np.zeros((2, batch_size, 64), dtype=np.float32)
-        return h, c
+    def get_initial_states(self, batch_size: int):
+        state = np.zeros((2, batch_size, 128), dtype=np.float32)
+        context = np.zeros((batch_size, 64), dtype=np.float32)
+        return state, context
 
-    def __call__(self, x, state, sr: int):
+    def __call__(self, x, state, context, sr: int):
         if len(x.shape) == 1:
             x = np.expand_dims(x, 0)
         if len(x.shape) > 2:
@@ -276,16 +272,325 @@ class SileroVADModel:
         if sr / x.shape[1] > 31.25:
             raise ValueError("Input audio chunk is too short")
 
-        h, c = state
+        x = np.concatenate([context, x], axis=1)
 
         ort_inputs = {
             "input": x,
-            "h": h,
-            "c": c,
+            "state": state,
             "sr": np.array(sr, dtype="int64"),
         }
 
-        out, h, c = self.session.run(None, ort_inputs)
-        state = (h, c)
+        out, state = self.session.run(None, ort_inputs)
+        context = x[..., -64:]
 
-        return out, state
+        return out, state, context
+
+
+# BSD 2-Clause License
+
+# Copyright (c) 2024, Max Bain
+
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+
+# 1. Redistributions of source code must retain the above copyright notice, this
+#    list of conditions and the following disclaimer.
+
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+#    this list of conditions and the following disclaimer in the documentation
+#    and/or other materials provided with the distribution.
+
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+# The code below is copied from whisper-x (https://github.com/m-bain/whisperX)
+# and adapted for faster_whisper.
+class SegmentX:
+    def __init__(self, start, end, speaker=None):
+        self.start = start
+        self.end = end
+        self.speaker = speaker
+
+
+class VoiceActivitySegmentation(VoiceActivityDetection, ABC):
+    """Pipeline wrapper class for Voice Activity Segmentation based on VAD scores."""
+
+    def __init__(
+        self,
+        segmentation: PipelineModel = "pyannote/segmentation",
+        device: Optional[Union[str, torch.device]] = None,
+        fscore: bool = False,
+        use_auth_token: Optional[str] = None,
+        **inference_kwargs,
+    ):
+        """Initialize the pipeline with the model name and the optional device.
+
+        Args:
+            dict parameters of VoiceActivityDetection class from pyannote:
+            segmentation (PipelineModel): Loaded model name.
+            device (torch.device or None): Device to perform the segmentation.
+            fscore (bool): Flag indicating whether to compute F-score during inference.
+            use_auth_token (str or None): Optional authentication token for model access.
+            inference_kwargs (dict):  Additional arguments from VoiceActivityDetection pipeline.
+        """
+        super().__init__(
+            segmentation=segmentation,
+            device=device,
+            fscore=fscore,
+            use_auth_token=use_auth_token,
+            **inference_kwargs,
+        )
+
+    def apply(
+        self, file: AudioFile, hook: Optional[Callable] = None
+    ) -> SlidingWindowFeature:
+        """Apply voice activity detection on the audio file.
+
+        Args:
+            file (AudioFile): Processed file.
+            hook (callable): Hook called with signature: hook("step_name", step_artefact, file=file)
+
+        Returns:
+            segmentations (SlidingWindowFeature): Voice activity segmentation.
+        """
+        # setup hook (e.g. for debugging purposes)
+        hook = self.setup_hook(file, hook=hook)
+
+        # apply segmentation model if needed
+        # output shape is (num_chunks, num_frames, 1)
+        if self.training:
+            if self.CACHED_SEGMENTATION in file:
+                segmentations = file[self.CACHED_SEGMENTATION]
+            else:
+                segmentations = self._segmentation(file)
+                file[self.CACHED_SEGMENTATION] = segmentations
+        else:
+            segmentations: SlidingWindowFeature = self._segmentation(file)
+
+        return segmentations
+
+
+class BinarizeVadScores:
+    """Binarize detection scores using hysteresis thresholding.
+
+    Reference:
+        Gregory Gelly and Jean-Luc Gauvain. "Minimum Word Error Training of
+        RNN-based Voice Activity Detection", InterSpeech 2015.
+
+        Modified by Max Bain to include WhisperX's min-cut operation
+        https://arxiv.org/abs/2303.00747
+
+    """
+
+    def __init__(
+        self,
+        onset: float = 0.5,
+        offset: Optional[float] = None,
+        min_duration_on: float = 0.0,
+        min_duration_off: float = 0.0,
+        pad_onset: float = 0.0,
+        pad_offset: float = 0.0,
+        max_duration: float = float("inf"),
+    ):
+        """Initializes the parameters for Binarizing the VAD scores.
+
+        Args:
+            onset (float, optional):
+                Onset threshold. Defaults to 0.5.
+            offset (float, optional):
+                Offset threshold. Defaults to `onset`.
+            min_duration_on (float, optional):
+                Remove active regions shorter than that many seconds. Defaults to 0s.
+            min_duration_off (float, optional):
+                Fill inactive regions shorter than that many seconds. Defaults to 0s.
+            pad_onset (float, optional):
+                Extend active regions by moving their start time by that many seconds.
+                Defaults to 0s.
+            pad_offset (float, optional):
+                Extend active regions by moving their end time by that many seconds.
+                Defaults to 0s.
+            max_duration (float):
+                The maximum length of an active segment.
+        """
+        super().__init__()
+
+        self.onset = onset
+        self.offset = offset or onset
+
+        self.pad_onset = pad_onset
+        self.pad_offset = pad_offset
+
+        self.min_duration_on = min_duration_on
+        self.min_duration_off = min_duration_off
+
+        self.max_duration = max_duration
+
+    def __get_active_regions(self, scores: SlidingWindowFeature) -> Annotation:
+        """Extract active regions from VAD scores.
+
+        Args:
+            scores (SlidingWindowFeature): Detection scores.
+
+        Returns:
+            active (Annotation): Active regions.
+        """
+        num_frames, num_classes = scores.data.shape
+        frames = scores.sliding_window
+        timestamps = [frames[i].middle for i in range(num_frames)]
+        # annotation meant to store 'active' regions
+        active = Annotation()
+        for k, k_scores in enumerate(scores.data.T):
+            label = k if scores.labels is None else scores.labels[k]
+
+            # initial state
+            start = timestamps[0]
+            is_active = k_scores[0] > self.onset
+            curr_scores = [k_scores[0]]
+            curr_timestamps = [start]
+            t = start
+            # optionally add `strict=False` for python 3.10 or later
+            for t, y in zip(timestamps[1:], k_scores[1:]):
+                # currently active
+                if is_active:
+                    curr_duration = t - start
+                    if curr_duration > self.max_duration:
+                        search_after = len(curr_scores) // 2
+                        # divide segment
+                        min_score_div_idx = search_after + np.argmin(
+                            curr_scores[search_after:]
+                        )
+                        min_score_t = curr_timestamps[min_score_div_idx]
+                        region = Segment(
+                            start - self.pad_onset, min_score_t + self.pad_offset
+                        )
+                        active[region, k] = label
+                        start = curr_timestamps[min_score_div_idx]
+                        curr_scores = curr_scores[min_score_div_idx + 1 :]
+                        curr_timestamps = curr_timestamps[min_score_div_idx + 1 :]
+                    # switching from active to inactive
+                    elif y < self.offset:
+                        region = Segment(start - self.pad_onset, t + self.pad_offset)
+                        active[region, k] = label
+                        start = t
+                        is_active = False
+                        curr_scores = []
+                        curr_timestamps = []
+                    curr_scores.append(y)
+                    curr_timestamps.append(t)
+                # currently inactive
+                else:
+                    # switching from inactive to active
+                    if y > self.onset:
+                        start = t
+                        is_active = True
+
+            # if active at the end, add final region
+            if is_active:
+                region = Segment(start - self.pad_onset, t + self.pad_offset)
+                active[region, k] = label
+
+        return active
+
+    def __call__(self, scores: SlidingWindowFeature) -> Annotation:
+        """Binarize detection scores.
+
+        Args:
+            scores (SlidingWindowFeature): Detection scores.
+
+        Returns:
+            active (Annotation): Binarized scores.
+        """
+        active = self.__get_active_regions(scores)
+        # because of padding, some active regions might be overlapping: merge them.
+        # also: fill same speaker gaps shorter than min_duration_off
+        if self.pad_offset > 0.0 or self.pad_onset > 0.0 or self.min_duration_off > 0.0:
+            if self.max_duration < float("inf"):
+                raise NotImplementedError("This would break current max_duration param")
+            active = active.support(collar=self.min_duration_off)
+
+        # remove tracks shorter than min_duration_on
+        if self.min_duration_on > 0:
+            for segment, track in list(active.itertracks()):
+                if segment.duration < self.min_duration_on:
+                    del active[segment, track]
+
+        return active
+
+
+def merge_chunks(
+    segments,
+    chunk_length,
+    onset: float = 0.5,
+    offset: Optional[float] = None,
+    edge_padding: float = 0.1,
+):
+    """
+    Merge operation described in whisper-x paper
+    """
+    curr_end = 0
+    merged_segments = []
+    seg_idxs = []
+    speaker_idxs = []
+
+    assert chunk_length > 0
+    binarize = BinarizeVadScores(max_duration=chunk_length, onset=onset, offset=offset)
+    segments = binarize(segments)
+    segments_list = []
+    for speech_turn in segments.get_timeline():
+        segments_list.append(
+            SegmentX(
+                max(0.0, speech_turn.start - edge_padding),
+                speech_turn.end + edge_padding,
+                "UNKNOWN",
+            )
+        )  # 100ms edge padding to account for edge errors
+
+    if len(segments_list) == 0:
+        print("No active speech found in audio")
+        return []
+
+    # Make sur the starting point is the start of the segment.
+    curr_start = segments_list[0].start
+
+    for idx, seg in enumerate(segments_list):
+        # if any segment start timing is less than previous segment end timing,
+        # reset the edge padding. Similarly for end timing.
+        if idx > 0:
+            if seg.start < segments_list[idx - 1].end:
+                seg.start += edge_padding
+        if idx < len(segments_list) - 1:
+            if seg.end > segments_list[idx + 1].start:
+                seg.end -= edge_padding
+
+        if seg.end - curr_start > chunk_length and curr_end - curr_start > 0:
+            merged_segments.append(
+                {
+                    "start": curr_start,
+                    "end": curr_end,
+                    "segments": seg_idxs,
+                }
+            )
+            curr_start = seg.start
+            seg_idxs = []
+            speaker_idxs = []
+        curr_end = seg.end
+        seg_idxs.append((seg.start, seg.end))
+        speaker_idxs.append(seg.speaker)
+    # add final
+    merged_segments.append(
+        {
+            "start": curr_start,
+            "end": curr_end,
+            "segments": seg_idxs,
+        }
+    )
+    return merged_segments

faster_whisper/version.py

@@ -1,3 +1,3 @@
 """Version information."""
 
-__version__ = "1.0.1"
+__version__ = "1.0.3"

requirements.txt

@@ -1,5 +1,8 @@
-av==11.*
 ctranslate2>=4.0,<5
 huggingface_hub>=0.13
-tokenizers>=0.13,<0.16
-onnxruntime>=1.14,<2
+tokenizers>=0.13,<1
+onnxruntime>=1.14,<2 
+pyannote-audio>=3.1.1
+torch>=2.1.1 
+torchaudio>=2.1.2
+tqdm

tests/conftest.py

@@ -11,3 +11,8 @@ def data_dir():
 @pytest.fixture
 def jfk_path(data_dir):
     return os.path.join(data_dir, "jfk.flac")
+
+
+@pytest.fixture
+def physcisworks_path(data_dir):
+    return os.path.join(data_dir, "physicsworks.wav")

tests/test_transcribe.py

@@ -1,6 +1,8 @@
 import os
 
-from faster_whisper import WhisperModel, decode_audio
+from faster_whisper import BatchedInferencePipeline, WhisperModel, decode_audio
+from faster_whisper.tokenizer import Tokenizer
+from faster_whisper.transcribe import get_suppressed_tokens
 
 
 def test_supported_languages():
@@ -37,6 +39,50 @@ def test_transcribe(jfk_path):
     assert segment.text == "".join(word.word for word in segment.words)
     assert segment.start == segment.words[0].start
     assert segment.end == segment.words[-1].end
+    batched_model = BatchedInferencePipeline(model=model, use_vad_model=False)
+    result, info = batched_model.transcribe(jfk_path, word_timestamps=True)
+    assert info.language == "en"
+    assert info.language_probability > 0.7
+    segments = []
+    for segment in result:
+        segments.append(
+            {"start": segment.start, "end": segment.end, "text": segment.text}
+        )
+
+    assert len(segments) == 1
+    assert segment.text == (
+        " And so my fellow Americans ask not what your country can do for you, "
+        "ask what you can do for your country."
+    )
+
+
+def test_batched_transcribe(physcisworks_path):
+    model = WhisperModel("tiny")
+    batched_model = BatchedInferencePipeline(model=model)
+    result, info = batched_model.transcribe(physcisworks_path, batch_size=16)
+    assert info.language == "en"
+    assert info.language_probability > 0.7
+    segments = []
+    for segment in result:
+        segments.append(
+            {"start": segment.start, "end": segment.end, "text": segment.text}
+        )
+    # number of near 30 sec segments
+    assert len(segments) == 8
+
+    result, info = batched_model.transcribe(
+        physcisworks_path,
+        batch_size=16,
+        without_timestamps=False,
+        word_timestamps=True,
+    )
+    segments = []
+    for segment in result:
+        assert segment.words is not None
+        segments.append(
+            {"start": segment.start, "end": segment.end, "text": segment.text}
+        )
+    assert len(segments) > 8
 
 
 def test_prefix_with_timestamps(jfk_path):
@@ -97,3 +143,116 @@ def test_stereo_diarization(data_dir):
     segments, _ = model.transcribe(right)
     transcription = "".join(segment.text for segment in segments).strip()
     assert transcription == "The horizon seems extremely distant."
+
+
+def test_multisegment_lang_id(physcisworks_path):
+    model = WhisperModel("tiny")
+    language_info = model.detect_language_multi_segment(physcisworks_path)
+    assert language_info["language_code"] == "en"
+    assert language_info["language_confidence"] > 0.8
+
+
+def test_suppressed_tokens_minus_1():
+    model = WhisperModel("tiny.en")
+
+    tokenizer = Tokenizer(model.hf_tokenizer, False)
+    tokens = get_suppressed_tokens(tokenizer, [-1])
+    assert tokens == (
+        1,
+        2,
+        7,
+        8,
+        9,
+        10,
+        14,
+        25,
+        26,
+        27,
+        28,
+        29,
+        31,
+        58,
+        59,
+        60,
+        61,
+        62,
+        63,
+        90,
+        91,
+        92,
+        93,
+        357,
+        366,
+        438,
+        532,
+        685,
+        705,
+        796,
+        930,
+        1058,
+        1220,
+        1267,
+        1279,
+        1303,
+        1343,
+        1377,
+        1391,
+        1635,
+        1782,
+        1875,
+        2162,
+        2361,
+        2488,
+        3467,
+        4008,
+        4211,
+        4600,
+        4808,
+        5299,
+        5855,
+        6329,
+        7203,
+        9609,
+        9959,
+        10563,
+        10786,
+        11420,
+        11709,
+        11907,
+        13163,
+        13697,
+        13700,
+        14808,
+        15306,
+        16410,
+        16791,
+        17992,
+        19203,
+        19510,
+        20724,
+        22305,
+        22935,
+        27007,
+        30109,
+        30420,
+        33409,
+        34949,
+        40283,
+        40493,
+        40549,
+        47282,
+        49146,
+        50257,
+        50357,
+        50358,
+        50359,
+        50360,
+    )
+
+
+def test_suppressed_tokens_minus_value():
+    model = WhisperModel("tiny.en")
+
+    tokenizer = Tokenizer(model.hf_tokenizer, False)
+    tokens = get_suppressed_tokens(tokenizer, [13])
+    assert tokens == (13, 50257, 50357, 50358, 50359, 50360)