Merge pull request #331 from alibaba-damo-academy/dev_lhn

update uniasr infer recipe

egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer.py

@@ -23,8 +23,7 @@ def modelscope_infer_core(output_dir, split_dir, njob, idx):
         batch_size=1
     )
     audio_in = os.path.join(split_dir, "wav.{}.scp".format(idx))
-    inference_pipline(audio_in=audio_in, param_dict={"decoding_model": "offline"})
-
+    inference_pipline(audio_in=audio_in)
 
 def modelscope_infer(params):
     # prepare for multi-GPU decoding

egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer_after_finetune.py

@@ -2,52 +2,103 @@ import json
 import os
 import shutil
 
+from multiprocessing import Pool
 from modelscope.pipelines import pipeline
 from modelscope.utils.constant import Tasks
 
 from funasr.utils.compute_wer import compute_wer
 
 
+def modelscope_infer_after_finetune_core(model_dir, output_dir, split_dir, njob, idx):
+    output_dir_job = os.path.join(output_dir, "output.{}".format(idx))
+    gpu_id = (int(idx) - 1) // njob
+    if "CUDA_VISIBLE_DEVICES" in os.environ.keys():
+        gpu_list = os.environ['CUDA_VISIBLE_DEVICES'].split(",")
+        os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_list[gpu_id])
+    else:
+        os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
+    inference_pipeline = pipeline(
+        task=Tasks.auto_speech_recognition,
+        model=model_dir,
+        output_dir=output_dir_job,
+        batch_size=1
+    )
+    audio_in = os.path.join(split_dir, "wav.{}.scp".format(idx))
+    inference_pipeline(audio_in=audio_in)
+
 def modelscope_infer_after_finetune(params):
-    # prepare for decoding
+    # prepare for multi-GPU decoding
+    model_dir = params["model_dir"]
     pretrained_model_path = os.path.join(os.environ["HOME"], ".cache/modelscope/hub", params["modelscope_model_name"])
     for file_name in params["required_files"]:
         if file_name == "configuration.json":
             with open(os.path.join(pretrained_model_path, file_name)) as f:
                 config_dict = json.load(f)
                 config_dict["model"]["am_model_name"] = params["decoding_model_name"]
-            with open(os.path.join(params["output_dir"], "configuration.json"), "w") as f:
+            with open(os.path.join(model_dir, "configuration.json"), "w") as f:
                 json.dump(config_dict, f, indent=4, separators=(',', ': '))
         else:
             shutil.copy(os.path.join(pretrained_model_path, file_name),
-                        os.path.join(params["output_dir"], file_name))
-    decoding_path = os.path.join(params["output_dir"], "decode_results")
-    if os.path.exists(decoding_path):
-        shutil.rmtree(decoding_path)
-    os.mkdir(decoding_path)
+                        os.path.join(model_dir, file_name))
+    ngpu = params["ngpu"]
+    njob = params["njob"]
+    output_dir = params["output_dir"]
+    if os.path.exists(output_dir):
+        shutil.rmtree(output_dir)
+    os.mkdir(output_dir)
+    split_dir = os.path.join(output_dir, "split")
+    os.mkdir(split_dir)
+    nj = ngpu * njob
+    wav_scp_file = os.path.join(params["data_dir"], "wav.scp")
+    with open(wav_scp_file) as f:
+        lines = f.readlines()
+        num_lines = len(lines)
+        num_job_lines = num_lines // nj
+    start = 0
+    for i in range(nj):
+        end = start + num_job_lines
+        file = os.path.join(split_dir, "wav.{}.scp".format(str(i + 1)))
+        with open(file, "w") as f:
+            if i == nj - 1:
+                f.writelines(lines[start:])
+            else:
+                f.writelines(lines[start:end])
+        start = end
 
-    # decoding
-    inference_pipeline = pipeline(
-        task=Tasks.auto_speech_recognition,
-        model=params["output_dir"],
-        output_dir=decoding_path,
-        batch_size=1
-    )
-    audio_in = os.path.join(params["data_dir"], "wav.scp")
-    inference_pipeline(audio_in=audio_in, param_dict={"decoding_model": "offline"})
+    p = Pool(nj)
+    for i in range(nj):
+        p.apply_async(modelscope_infer_after_finetune_core,
+                      args=(model_dir, output_dir, split_dir, njob, str(i + 1)))
+    p.close()
+    p.join()
 
-    # computer CER if GT text is set
+    # combine decoding results
+    best_recog_path = os.path.join(output_dir, "1best_recog")
+    os.mkdir(best_recog_path)
+    files = ["text", "token", "score"]
+    for file in files:
+        with open(os.path.join(best_recog_path, file), "w") as f:
+            for i in range(nj):
+                job_file = os.path.join(output_dir, "output.{}/1best_recog".format(str(i + 1)), file)
+                with open(job_file) as f_job:
+                    lines = f_job.readlines()
+                f.writelines(lines)
+
+    # If text exists, compute CER
     text_in = os.path.join(params["data_dir"], "text")
     if os.path.exists(text_in):
-        text_proc_file = os.path.join(decoding_path, "1best_recog/text")
-        compute_wer(text_in, text_proc_file, os.path.join(decoding_path, "text.cer"))
-
+        text_proc_file = os.path.join(best_recog_path, "token")
+        compute_wer(text_in, text_proc_file, os.path.join(best_recog_path, "text.cer"))
 
 if __name__ == '__main__':
     params = {}
     params["modelscope_model_name"] = "damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline"
     params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
-    params["output_dir"] = "./checkpoint"
+    params["model_dir"] = "./checkpoint"
+    params["output_dir"] = "./results"
     params["data_dir"] = "./data/test"
     params["decoding_model_name"] = "20epoch.pb"
+    params["ngpu"] = 1
+    params["njob"] = 1
     modelscope_infer_after_finetune(params)
+