From 0a51365f8af7d034c5fddf8fa7c9e810f3296d17 Mon Sep 17 00:00:00 2001 From: nichongjia-2007 <nichongjia@gmail.com> Date: 星期四, 11 五月 2023 18:28:26 +0800 Subject: [PATCH] Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR --- docs/m2met2/_build/html/_sources/Baseline.md.txt | 3 ++- 1 files changed, 2 insertions(+), 1 deletions(-) diff --git a/docs/m2met2/_build/html/_sources/Baseline.md.txt b/docs/m2met2/_build/html/_sources/Baseline.md.txt index 728a375..4e12162 100644 --- a/docs/m2met2/_build/html/_sources/Baseline.md.txt +++ b/docs/m2met2/_build/html/_sources/Baseline.md.txt @@ -16,6 +16,7 @@ |鈥斺�� Test_Ali_near |鈥斺�� Train_Ali_far |鈥斺�� Train_Ali_near +``` Before running `run_m2met_2023_infer.sh`, you need to place the new test set `Test_2023_Ali_far` (to be released after the challenge starts) in the `./dataset` directory, which contains only raw audios. Then put the given `wav.scp`, `wav_raw.scp`, `segments`, `utt2spk` and `spk2utt` in the `./data/Test_2023_Ali_far` directory. ```shell data/Test_2023_Ali_far @@ -30,4 +31,4 @@ ## Baseline results The results of the baseline system are shown in Table 3. The speaker profile adopts the oracle speaker embedding during training. However, due to the lack of oracle speaker label during evaluation, the speaker profile provided by an additional spectral clustering is used. Meanwhile, the results of using the oracle speaker profile on Eval and Test Set are also provided to show the impact of speaker profile accuracy. - \ No newline at end of file + \ No newline at end of file -- Gitblit v1.9.1