From cbd2e535db44709fc87adf24f8e63580980e69ff Mon Sep 17 00:00:00 2001 From: Peter Staar Date: Mon, 16 Jun 2025 19:06:47 +0200 Subject: [PATCH] first working ASR pipeline Signed-off-by: Peter Staar --- docling/cli/main.py | 24 +- docling/pipeline/asr_pipeline.py | 403 +++++++++---- pyproject.toml | 2 + uv.lock | 983 +++++++++++++++++++++++++++++++ 4 files changed, 1292 insertions(+), 120 deletions(-) diff --git a/docling/cli/main.py b/docling/cli/main.py index b93337aa..7ad810f1 100644 --- a/docling/cli/main.py +++ b/docling/cli/main.py @@ -48,6 +48,7 @@ from docling.datamodel.pipeline_options import ( PaginatedPipelineOptions, PdfBackend, PdfPipelineOptions, + PipelineOptions, ProcessingPipeline, TableFormerMode, VlmPipelineOptions, @@ -466,12 +467,14 @@ def convert( # noqa: C901 ), ] = None, ): + log_format = "%(asctime)s\t%(levelname)s\t%(name)s: %(message)s" + if verbose == 0: - logging.basicConfig(level=logging.WARNING) + logging.basicConfig(level=logging.WARNING, format=log_format) elif verbose == 1: - logging.basicConfig(level=logging.INFO) + logging.basicConfig(level=logging.INFO, format=log_format) else: - logging.basicConfig(level=logging.DEBUG) + logging.basicConfig(level=logging.DEBUG, format=log_format) settings.debug.visualize_cells = debug_visualize_cells settings.debug.visualize_layout = debug_visualize_layout @@ -546,7 +549,8 @@ def convert( # noqa: C901 ocr_options.lang = ocr_lang_list accelerator_options = AcceleratorOptions(num_threads=num_threads, device=device) - pipeline_options: PaginatedPipelineOptions + # pipeline_options: PaginatedPipelineOptions + pipeline_options: PipelineOptions format_options: Dict[InputFormat, FormatOption] = {} @@ -593,7 +597,7 @@ def convert( # noqa: C901 backend=backend, # pdf_backend ) - format_options: Dict[InputFormat, FormatOption] = { + format_options = { InputFormat.PDF: pdf_format_option, InputFormat.IMAGE: pdf_format_option, } @@ -624,7 +628,7 @@ def convert( # noqa: C901 pipeline_cls=VlmPipeline, pipeline_options=pipeline_options ) - format_options: Dict[InputFormat, FormatOption] = { + format_options = { InputFormat.PDF: pdf_format_option, InputFormat.IMAGE: pdf_format_option, } @@ -638,6 +642,7 @@ def convert( # noqa: C901 if asr_model == AsrModelType.WHISPER_TINY: pipeline_options.asr_options = WHISPER_TINY else: + _log.warning("falling back in base ASR model: WHISPER_TINY") pipeline_options.asr_options = WHISPER_TINY audio_format_option = AudioFormatOption( @@ -646,15 +651,10 @@ def convert( # noqa: C901 backend=AudioBackend, ) - format_options: Dict[InputFormat, FormatOption] = { + format_options = { InputFormat.AUDIO_WAV: audio_format_option, } - """ - if asr_model == AsrModelType.WHISPER_TINY: - pipeline_options.asr_options = WHISPER_TINY: - """ - if artifacts_path is not None: pipeline_options.artifacts_path = artifacts_path # audio_pipeline_options.artifacts_path = artifacts_path diff --git a/docling/pipeline/asr_pipeline.py b/docling/pipeline/asr_pipeline.py index e428a94a..173beffc 100644 --- a/docling/pipeline/asr_pipeline.py +++ b/docling/pipeline/asr_pipeline.py @@ -5,9 +5,13 @@ from io import BytesIO from pathlib import Path from typing import List, Optional, Union, cast -import soundfile as sf +import librosa # type: ignore +import numpy as np +import soundfile as sf # type: ignore from docling_core.types.doc.labels import DocItemLabel from pydantic import BaseModel, Field, validator +from pydub import AudioSegment # type: ignore +from transformers import WhisperForConditionalGeneration, WhisperProcessor, pipeline from docling.backend.abstract_backend import AbstractDocumentBackend from docling.backend.audio_backend import AudioBackend @@ -29,58 +33,297 @@ from docling.datamodel.settings import settings from docling.pipeline.base_pipeline import BasePipeline from docling.utils.profiling import ProfilingScope, TimeRecorder -import librosa - _log = logging.getLogger(__name__) -class ConversationEntry(BaseModel): +class _ConversationItem(BaseModel): text: str - start_time: float = Field( - ..., ge=0, description="Start time in seconds from video start" + start_time: Optional[float] = Field( + None, description="Start time in seconds from video start" ) - end_time: float = Field( - ..., ge=0, description="End time in seconds from video start" + end_time: Optional[float] = Field( + None, ge=0, description="End time in seconds from video start" ) - speaker_id: int = Field(..., ge=0, description="Numeric speaker identifier") + speaker_id: Optional[int] = Field(None, description="Numeric speaker identifier") speaker: Optional[str] = Field( None, description="Speaker name, defaults to speaker-{speaker_id}" ) - @validator("end_time") - def end_time_must_be_after_start(cls, v, values): - if "start_time" in values and v <= values["start_time"]: - raise ValueError("end_time must be greater than start_time") - return v - - @validator("speaker", always=True) - def set_default_speaker_name(cls, v, values): - if v is None and "speaker_id" in values: - return f"speaker-{values['speaker_id']}" - return v - def __lt__(self, other): - if not isinstance(other, ConversationEntry): + if not isinstance(other, _ConversationItem): return NotImplemented return self.start_time < other.start_time def __eq__(self, other): - if not isinstance(other, ConversationEntry): + if not isinstance(other, _ConversationItem): return NotImplemented return self.start_time == other.start_time def to_string(self) -> str: """Format the conversation entry as a string""" - return f"[time: {self.start_time}-{self.end_time}] [speaker:{self.speaker}] {self.text}" + result = "" + if (self.start_time is not None) and (self.end_time is not None): + result += f"[time: {self.start_time}-{self.end_time}] " + + if self.speaker is not None: + result += f"[speaker:{self.speaker}] " + + result += self.text + return result + + +class _WhisperASR: + def __init__(self, model_name: str = "openai/whisper-small"): + """ + Transcriber using Hugging Face Transformers Whisper + energy-based VAD. + """ + print(f"Loading Whisper model: {model_name}") + + self.device = "cpu" + + self.transcriber = pipeline( + "automatic-speech-recognition", + model=model_name, + return_timestamps=True, + device=self.device, + ) + + def _energy_vad( + self, + y: np.ndarray, + sr: int, + frame_length=2048, + hop_length=512, + threshold_percentile=85, + ): + """ + Simple energy-based VAD. + Returns list of (start_time, end_time) tuples for speech segments. + """ + _log.debug(f"_energy_vad {sr}: ", y.shape) + energy = np.array( + [ + np.sum(np.abs(y[i : i + frame_length] ** 2)) + for i in range(0, len(y), hop_length) + ] + ) + _log.debug(f"energy: {energy}") + + threshold = np.percentile(energy, threshold_percentile) * 0.3 + _log.debug(f"threshold: {threshold}") + + speech_frames = energy > threshold + _log.debug(f"speech_frames: {speech_frames}") + + frame_times = librosa.frames_to_time( + np.arange(len(energy)), sr=sr, hop_length=hop_length + ) + + segments = [] + start_time = None + + for i, is_speech in enumerate(speech_frames): + t = frame_times[i] + if is_speech and start_time is None: + start_time = t + elif not is_speech and start_time is not None: + segments.append((start_time, t)) + start_time = None + + if start_time is not None: + segments.append((start_time, frame_times[-1])) + + return segments + + def _merge_vad_segments(self, segments, min_duration=5.0, max_gap=0.5): + """ + Merge short/adjacent speech segments to improve transcription quality. + """ + if not segments: + return [] + + merged = [] + current_start, current_end = segments[0] + + for start, end in segments[1:]: + gap = start - current_end + if gap <= max_gap or (current_end - current_start) < min_duration: + current_end = end # merge + else: + if current_end - current_start >= 1.0: # skip ultra-short + merged.append((current_start, current_end)) + current_start, current_end = start, end + + if current_end - current_start >= 1.0: + merged.append((current_start, current_end)) + + return merged + + def run(self, conv_res: ConversionResult) -> ConversionResult: + """ + Transcribe audio using custom VAD and Whisper, returning timestamped segments. + Returns list of {"start", "end", "text"} dictionaries. + """ + audio_path = conv_res.input.file + + _log.info(f"Loading audio and resampling: {audio_path}") + y, sr = librosa.load(audio_path, sr=16000) + + speech_segments = self._energy_vad(y=y, sr=int(sr)) + speech_segments = self._merge_vad_segments(speech_segments) + _log.info("#-speech: ", len(speech_segments)) + + _log.info("Preparing AudioSegment for chunk slicing...") + pcm = (y * 32767).astype(np.int16).tobytes() + audio_seg = AudioSegment(data=pcm, sample_width=2, frame_rate=16000, channels=1) + + result = self._create_conversation_entries_v2(speech_segments, audio_seg) + result.sort() + + for _ in result: + conv_res.document.add_text(label=DocItemLabel.TEXT, text=_.to_string()) + + conv_res.status = ConversionStatus.SUCCESS + return conv_res + + def _create_conversation_entries_v1( + self, speech_segments, audio_seg + ) -> list[_ConversationItem]: + """ + Chunk audio based on speech_segments, transcribe with Whisper, + and return structured _ConversationItem items. + """ + results = [] + chunk_id = 0 + + for start, end in speech_segments: + duration = end - start + while duration > 0: + sub_end = min(start + 30.0, end) + chunk = audio_seg[start * 1000 : sub_end * 1000] + samples = ( + np.array(chunk.get_array_of_samples()).astype(np.float32) / 32768.0 + ) + + try: + _log.debug( + f"Transcribing chunk {chunk_id}: {start:.2f}s - {sub_end:.2f}s [{sub_end - start:.2f}]" + ) + result = self.transcriber(samples, return_timestamps=True) + + # Adjust timestamps globally + for seg in result["chunks"]: + t0, t1 = seg["timestamp"] + if t0 is None or t1 is None or t1 <= t0: + _log.warning(f"skipping bad segment: {seg}") + continue + + item = _ConversationItem( + text=seg["text"].strip(), + start_time=start + t0, + end_time=start + t1, + ) + results.append(item) + + start = sub_end + duration = end - start + chunk_id += 1 + except Exception as exc: + _log.error(f"Exception: {exc}") + + return results + + def _create_conversation_entries_v2( + self, speech_segments, audio_seg + ) -> list[_ConversationItem]: + """ + Chunk audio based on speech_segments, transcribe with Whisper, + and return structured _ConversationItem items. + """ + results = [] + chunk_id = 0 + + if len(speech_segments) == 0: + return [] + + any_valid = False + last_valid_offset: float = speech_segments[0][0] + + for start, end in speech_segments: + if any_valid: + last_valid_offset = min(start, last_valid_offset) + else: + last_valid_offset = start + + duration = end - last_valid_offset + + if duration > 0.2: + sub_end = min(last_valid_offset + 30.0, end) + + chunk_i0 = int(last_valid_offset * 1000) + chunk_i1 = int(sub_end * 1000) + + chunk = audio_seg[chunk_i0:chunk_i1] + samples = ( + np.array(chunk.get_array_of_samples()).astype(np.float32) / 32768.0 + ) + chunk_id += 1 + + try: + result = self.transcriber(samples, return_timestamps=True) + + any_valid = False + + last_valid_offset_ = last_valid_offset + + for seg in result["chunks"]: + t0, t1 = seg["timestamp"] + if t0 is None or t1 is None or t1 <= t0: + _log.warning(f" => skipping bad segment: {seg}") + continue + + global_start = round(last_valid_offset_ + t0, 2) + global_end = round(last_valid_offset_ + t1, 2) + text = seg["text"].strip() + + results.append( + _ConversationItem( + start_time=global_start, end_time=global_end, text=text + ) + ) + last_valid_offset = max(global_end, last_valid_offset) + any_valid = True + + if not any_valid: + _log.warning( + "No valid transcription in chunk, nudging forward 1s." + ) + last_valid_offset += 1.0 + + except Exception as e: + _log.error(f"Whisper failed: {e}") + last_valid_offset += 1.0 + + duration = end - last_valid_offset + else: + any_valid = False + + return results class _WhisperModel: def __init__(self): _log.info("initialisation `_WhisperModel`") - from transformers import WhisperForConditionalGeneration, WhisperProcessor + self.device = "cpu" + self.chunk_length = 30 - self.model_repo = "openai/whisper-tiny" + self.batch_size = 8 + + # self.model_repo = "openai/whisper-tiny" + # self.model_repo = "openai/whisper-small" + self.model_repo = "openai/whisper-medium" + # self.model_repo = "openai/whisper-large" self.processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") self.model = WhisperForConditionalGeneration.from_pretrained( @@ -89,100 +332,43 @@ class _WhisperModel: # FIXME self.max_new_tokens = 256 - + _log.info(f"model is loaded: {self.model_repo}") - def run(self, conv_res: ConversionResult): - # fpath = Path(conv_res.input.file) - # _log.info(f"`_WhisperModel::run: {conv_res}`") - _log.info(f"`_WhisperModel::run: {conv_res.input}`") - _log.info(f"`_WhisperModel::run: {conv_res.input.file}`") + self.pipe = pipeline( + "automatic-speech-recognition", + model=self.model_repo, + chunk_length_s=self.chunk_length, + device=self.device, + ) - if os.path.exists(str(conv_res.input.file)): - print("file exists") - else: - print("file does not exist") - # - - _log.info(f"sampling-rate: {self.processor.feature_extractor.sampling_rate}") + def run(self, conv_res: ConversionResult) -> ConversionResult: + return self._run_pipeline(conv_res=conv_res) + def _run_pipeline(self, conv_res: ConversionResult) -> ConversionResult: try: fpath = conv_res.input.file - # array, sampling_rate = sf.read(fpath)#, samplerate=processor.feature_extractor.sampling_rate) - array, sampling_rate = sf.read( - fpath - ) # , samplerate=self.processor.feature_extractor.sampling_rate) - - _log.info( - f"read the file .. (sampling-rate: {sampling_rate}, array: {array.shape})" - ) array, sampling_rate = librosa.load(fpath, sr=16000) - _log.info( - f"read the file .. (sampling-rate: {sampling_rate}, array: {array.shape})" - ) + prediction = self.pipe( + inputs=array, batch_size=self.batch_size, return_timestamps=True + ) # ["chunks"] - - processed_input = self.processor( - array, - sampling_rate=self.processor.feature_extractor.sampling_rate, # sampling_rate, - return_tensors="pt", - ) - print(processed_input) - - # pre-process to get the input features - input_features = self.processor( - array, sampling_rate=sampling_rate, return_tensors="pt" - ).input_features - - _log.info(f"got input-features: {input_features.shape}") - _log.info(f"max new tokens: {self.max_new_tokens}") - - # generate token ids by running model forward sequentially - predicted_ids = self.model.generate( - input_features, max_new_tokens=self.max_new_tokens, return_timestamps=True - ) - - _log.info("ran model ..") - - """ - transcription = self.processor.batch_decode(predicted_ids, - skip_special_tokens=False, - decode_with_timestamps=True) - - _log.info("decoded output ..") - - print(f"Transcription: {transcription}") - """ - - conversation = [] - - print("Timestamp info:") - for pidi, pid in enumerate(predicted_ids): - # timestamps = processor.tokenizer.decode(pid, decode_with_timestamps=True) - timestamps = self.processor.tokenizer.decode(pid, output_offsets=True) - print(f"Predicted id [{pidi}]: {timestamps['text']}") - for offset in timestamps["offsets"]: - print(f" => {offset['timestamp']}: {offset['text']}") - - item = ConversationEntry( - text=offset["text"], - speaker_id=pidi, - start_time=offset["timestamp"][0], - end_time=offset["timestamp"][1], - ) - conv_res.document.add_text( - label=DocItemLabel.TEXT, text=item.to_string() - ) + for _ in prediction["chunks"]: + item = _ConversationItem( + text=_["text"], + start_time=_["timestamp"][0], + end_time=_["timestamp"][1], + ) + conv_res.document.add_text( + label=DocItemLabel.TEXT, text=item.to_string() + ) conv_res.status = ConversionStatus.SUCCESS - - print("document: \n\n", conv_res.document.export_to_markdown()) - except Exception as exc: - conv_res.status = ConversionStatus.FAILED - print(exc) + conv_res.status = ConversionStatus.FAILURE + _log.error(f"Failed to convert with {self.model_repo}: {exc}") return conv_res @@ -206,7 +392,8 @@ class AsrPipeline(BasePipeline): "When defined, it must point to a folder containing all models required by the pipeline." ) - self._model = _WhisperModel() + # self._model = _WhisperModel() + self._model = _WhisperASR() def _determine_status(self, conv_res: ConversionResult) -> ConversionStatus: status = ConversionStatus.SUCCESS diff --git a/pyproject.toml b/pyproject.toml index cf02ed18..c4c4b2b2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -70,6 +70,8 @@ dependencies = [ 'scipy (>=1.6.0,<2.0.0)', # 'scipy (>=1.6.0,<2.0.0) ; python_version >= "3.10"', # 'scipy (>=1.6.0,<1.14.0) ; python_version < "3.10"', + "pydub[asr]>=0.25.1", + "pyannote-audio[asr]>=1.1.2", ] [project.urls] diff --git a/uv.lock b/uv.lock index 0ee85f86..58e736c6 100644 --- a/uv.lock +++ b/uv.lock @@ -27,6 +27,15 @@ resolution-markers = [ "(python_full_version < '3.10' and platform_machine != 'arm64' and sys_platform == 'darwin') or (python_full_version < '3.10' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.10' and sys_platform != 'darwin' and sys_platform != 'linux')", ] +[[package]] +name = "absl-py" +version = "2.3.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/03/15/18693af986560a5c3cc0b84a8046b536ffb2cdb536e03cce897f2759e284/absl_py-2.3.0.tar.gz", hash = "sha256:d96fda5c884f1b22178852f30ffa85766d50b99e00775ea626c23304f582fc4f", size = 116400, upload-time = "2025-05-27T09:15:50.143Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/87/04/9d75e1d3bb4ab8ec67ff10919476ccdee06c098bcfcf3a352da5f985171d/absl_py-2.3.0-py3-none-any.whl", hash = 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