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* fix(qwen-asr): enable timestamp output when forced_aligner is configured Two bugs prevented timestamps from working in the qwen-asr backend: 1. transcribe() was called without return_time_stamps=True, so the forced aligner was loaded but never invoked. Now we pass return_time_stamps=True when a forced_aligner is present. 2. The timestamp parsing code expected (list, tuple) items, but the qwen_asr library returns ForcedAlignItem dataclass instances with .text, .start_time, .end_time attributes. Added hasattr() check to handle this correctly, falling back to tuple parsing for backward compatibility. * refactor: address Copilot review for qwen-asr timestamps - Wrap return_time_stamps kwarg in try/except TypeError for safety - Add defensive float() normalization for timestamp times - Use str() for text extraction to ensure string type * fix(qwen-asr): convert seconds to nanoseconds for Go time.Duration The Go server reads TranscriptSegment.start/end via time.Duration, which is in nanoseconds. Previously the backend sent milliseconds (* 1000), causing timestamps to be 1000x too small (e.g. 8e-8 instead of 0.08). Convert seconds → nanoseconds (* 1e9) instead. Also applies to the legacy tuple path for consistency. * feat(qwen-asr): respect timestamp_granularities (segment vs word) Read request.timestamp_granularities from the gRPC request. - 'word': return one segment per aligned item (character / word) - 'segment' (default): merge consecutive items at sentence boundaries Sentence boundaries detected via CJK punctuation (。！？；…) and Latin endings (. ! ? ;). This matches the OpenAI Whisper API contract where omitting the parameter defaults to segment-level. * fix(qwen-asr): escape smart quotes in punctuation set Unicode curly quotes (U+2018/2019) were being interpreted as Python string delimiters, causing SyntaxError. Use explicit unicode escapes. * fix(qwen-asr): use time-gap threshold for segment boundaries The forced aligner strips punctuation from its output, so text-based sentence detection doesn't work. Instead, detect segment boundaries by measuring time gaps between consecutive aligned items. Threshold = max(median_gap * 4, 0.3s). This cleanly separates intra-sentence gaps (< 0.24s) from inter-sentence gaps (> 0.3s) across Chinese, English, and other languages. * fix(qwen-asr): smart join with spaces for non-CJK tokens The forced aligner strips whitespace from tokenized text, so English words like ['hello', 'world'] were joined as 'helloworld'. Add _smart_join() that inserts spaces between non-CJK tokens while keeping CJK characters and punctuation unspaced. Works for Chinese, English, Korean, Japanese, and mixed-language text. --------- Co-authored-by: fqscfqj <fqsfqj@outlook.com>