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Merged
pull merged 2 commits into from
Oct 1, 2025
Merged

[pull] main from m-bain:main #46

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83afb81
fix: restrict pyannote-audio version to avoid compatibility issues (#…
Barabazs Oct 1, 2025
c7d3188
Add jr, sr, and ph.d to punkt abbreviations
alexcannan Feb 18, 2025
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4 changes: 2 additions & 2 deletions pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
urls = { repository = "https://github.com/m-bain/whisperx" }
authors = [{ name = "Max Bain" }]
name = "whisperx"
version = "3.4.2"
version = "3.4.3"
description = "Time-Accurate Automatic Speech Recognition using Whisper."
readme = "README.md"
requires-python = ">=3.9, <3.13"
Expand All @@ -15,7 +15,7 @@ dependencies = [
"numpy>=2.0.2",
"onnxruntime>=1.19",
"pandas>=2.2.3",
"pyannote-audio>=3.3.2",
"pyannote-audio>=3.3.2,<4.0.0",
"torch>=2.5.1",
"torchaudio>=2.5.1",
"transformers>=4.48.0",
Expand Down
6 changes: 3 additions & 3 deletions uv.lock
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26 changes: 13 additions & 13 deletions whisperx/alignment.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,7 @@
)
from nltk.tokenize.punkt import PunktSentenceTokenizer, PunktParameters

PUNKT_ABBREVIATIONS = ['dr', 'vs', 'mr', 'mrs', 'prof']
PUNKT_ABBREVIATIONS = ['dr', 'vs', 'mr', 'mrs', 'prof', 'jr', 'sr', 'ph.d']

LANGUAGES_WITHOUT_SPACES = ["ja", "zh"]

Expand Down Expand Up @@ -124,14 +124,14 @@ def align(
"""
Align phoneme recognition predictions to known transcription.
"""

if not torch.is_tensor(audio):
if isinstance(audio, str):
audio = load_audio(audio)
audio = torch.from_numpy(audio)
if len(audio.shape) == 1:
audio = audio.unsqueeze(0)

MAX_DURATION = audio.shape[1] / SAMPLE_RATE

model_dictionary = align_model_metadata["dictionary"]
Expand All @@ -148,7 +148,7 @@ def align(
base_progress = ((sdx + 1) / total_segments) * 100
percent_complete = (50 + base_progress / 2) if combined_progress else base_progress
print(f"Progress: {percent_complete:.2f}%...")

num_leading = len(segment["text"]) - len(segment["text"].lstrip())
num_trailing = len(segment["text"]) - len(segment["text"].rstrip())
text = segment["text"]
Expand All @@ -165,7 +165,7 @@ def align(
# wav2vec2 models use "|" character to represent spaces
if model_lang not in LANGUAGES_WITHOUT_SPACES:
char_ = char_.replace(" ", "|")

# ignore whitespace at beginning and end of transcript
if cdx < num_leading:
pass
Expand All @@ -187,7 +187,7 @@ def align(
# index for placeholder
clean_wdx.append(wdx)


punkt_param = PunktParameters()
punkt_param.abbrev_types = set(PUNKT_ABBREVIATIONS)
sentence_splitter = PunktSentenceTokenizer(punkt_param)
Expand All @@ -199,12 +199,12 @@ def align(
"clean_wdx": clean_wdx,
"sentence_spans": sentence_spans
}

aligned_segments: List[SingleAlignedSegment] = []

# 2. Get prediction matrix from alignment model & align
for sdx, segment in enumerate(transcript):

t1 = segment["start"]
t2 = segment["end"]
text = segment["text"]
Expand Down Expand Up @@ -247,7 +247,7 @@ def align(
)
else:
lengths = None

with torch.inference_mode():
if model_type == "torchaudio":
emissions, _ = model(waveform_segment.to(device), lengths=lengths)
Expand Down Expand Up @@ -304,7 +304,7 @@ def align(
word_idx += 1
elif cdx == len(text) - 1 or text[cdx+1] == " ":
word_idx += 1

char_segments_arr = pd.DataFrame(char_segments_arr)

aligned_subsegments = []
Expand Down Expand Up @@ -333,7 +333,7 @@ def align(
word_end = word_chars["end"].max()
word_score = round(word_chars["score"].mean(), 3)

# -1 indicates unalignable
# -1 indicates unalignable
word_segment = {"word": word_text}

if not np.isnan(word_start):
Expand All @@ -344,7 +344,7 @@ def align(
word_segment["score"] = word_score

sentence_words.append(word_segment)

aligned_subsegments.append({
"text": sentence_text,
"start": sentence_start,
Expand Down