word_timestamps = parser.isSet(word_timestamp_option) output_formats: typing.Set[OutputFormat] = set() if parser.isSet(srt_option): output_formats.add(OutputFormat.SRT) @@ -192,6 +198,7 @@ def parse(app: Application, parser: QCommandLineParser): task=task, language=language, initial_pr...
timestamp_begin) * 0.02:.2f}|>" outputs.append(timestamp) outputs.append([]) else: outputs[-1].append(token) return "".join( [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs] ) def split_to_word_tokens( self, tokens: List[int] ) -> Tuple[List[str],...
程序集: Microsoft.CognitiveServices.Speech.csharp.dll 包: Microsoft.CognitiveServices.Speech v1.38.0 包括单词级时间戳。 启用音频日志记录后,此方法会将有关每个单词的起点和持续时间的时间详细信息添加到日志中。在 1.5.0 中添加 C# 复制 public void RequestWordLevelTimestamps (); 适用于 产...
On a side note, an alternative to whisper isWhisper-X, developed by Oxford researchers, is a word-level timestamps solution that enhances transcriptions and timestamps along with features like speaker diarization and fast batched inference. However, its limitation lies in supporting only a select...
With 30 minutes audio file (podcast - just talking, no music) at about 19th minute timestamps for words are reseted, but offset and duration for whole block is correct. Previous block has the same offset for whole block as well as for first word in this block, but here (you can...
atimestamps are available then this is displayed in an additional column, next to each log entry. 时间戳是可利用的这然后被显示在一个另外的专栏,在每日志项旁边。 [translate] aCP current source information, including influencing foreign CP systems such as: CP当前来源信息,包括影响外国CP系统例如: ...
Include word-level timestamps. When audio logging is enabled, this method adds time details about the start point and duration of each word to the log. Added in 1.5.0 C# 복사 public void RequestWordLevelTimestamps (); Applies to 제품버전 Azure SDK for .NET Latest ...
end = round(time_offset + timing.end, 2) segment["words"].append( dict(word=timing.word, start=start, end=end, probability=timing.probability) ) for segment in segments: if len(words := segment["words"]) > 0: # adjust the segment-level timestamps based on the word-level timestamp...
return_timestamps=True, return_token_timestamps=True, num_beams=3, num_return_sequences=2, ) self.assertEqual(generate_outputs.sequences.shape, generate_outputs.token_timestamps.shape) # should return num_samples*num_return_sequences (4*2) self.assertEqual(len(generate_outputs.se...
An alternative relevant approach to recovering word-level timestamps involves using wav2vec models that predict characters, as successfully implemented inwhisperX. However, these approaches have several drawbacks that are not present in approaches based on cross-attention weights such aswhisper_timestamp...