Merge branch 'alibaba-damo-academy:main' into add-offline-websocket-srv

zhaomingwork

2023-04-28 7a2ca2cca4e164e8c5ed10f381b6407751603b71

Merge branch 'alibaba-damo-academy:main' into add-offline-websocket-srv

 egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py

New file
@@ -0,0 +1,39 @@
import os
import logging
import torch
import soundfile

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.utils.logger import get_logger

logger = get_logger(log_level=logging.CRITICAL)
logger.setLevel(logging.CRITICAL)

os.environ["MODELSCOPE_CACHE"] = "./"
inference_pipeline = pipeline(
    task=Tasks.auto_speech_recognition,
    model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online',
    model_revision='v1.0.4'
)

model_dir = os.path.join(os.environ["MODELSCOPE_CACHE"], "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online")
speech, sample_rate = soundfile.read(os.path.join(model_dir, "example/asr_example.wav"))
speech_length = speech.shape[0]

sample_offset = 0
chunk_size = [5, 10, 5] #[5, 10, 5] 600ms, [8, 8, 4] 480ms
stride_size =  chunk_size[1] * 960
param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size}
final_result = ""

for sample_offset in range(0, speech_length, min(stride_size, speech_length - sample_offset)):
    if sample_offset + stride_size >= speech_length - 1:
        stride_size = speech_length - sample_offset
        param_dict["is_final"] = True
    rec_result = inference_pipeline(audio_in=speech[sample_offset: sample_offset + stride_size],
                                    param_dict=param_dict)
    if len(rec_result) != 0:
        final_result += rec_result['text'][0]
        print(rec_result)
print(final_result)

 egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py

@@ -14,24 +14,26 @@
inference_pipeline = pipeline(
    task=Tasks.auto_speech_recognition,
    model='damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online',
    model_revision='v1.0.2')
    model_revision='v1.0.4'
)

model_dir = os.path.join(os.environ["MODELSCOPE_CACHE"], "damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online")
speech, sample_rate = soundfile.read(os.path.join(model_dir, "example/asr_example.wav"))
speech_length = speech.shape[0]

sample_offset = 0
step = 4800  #300ms
param_dict = {"cache": dict(), "is_final": False}
chunk_size = [8, 8, 4] #[5, 10, 5] 600ms, [8, 8, 4] 480ms
stride_size =  chunk_size[1] * 960
param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size}
final_result = ""

for sample_offset in range(0, speech_length, min(step, speech_length - sample_offset)):
    if sample_offset + step >= speech_length - 1:
        step = speech_length - sample_offset
for sample_offset in range(0, speech_length, min(stride_size, speech_length - sample_offset)):
    if sample_offset + stride_size >= speech_length - 1:
        stride_size = speech_length - sample_offset
        param_dict["is_final"] = True
    rec_result = inference_pipeline(audio_in=speech[sample_offset: sample_offset + step],
    rec_result = inference_pipeline(audio_in=speech[sample_offset: sample_offset + stride_size],
                                    param_dict=param_dict)
    if len(rec_result) != 0 and rec_result['text'] != "sil" and rec_result['text'] != "waiting_for_more_voice":
        final_result += rec_result['text']
    print(rec_result)
    if len(rec_result) != 0:
        final_result += rec_result['text'][0]
        print(rec_result)
print(final_result)

 funasr/bin/asr_inference_paraformer_streaming.py

@@ -205,9 +205,12 @@
        results = []
        cache_en = cache["encoder"]
        if speech.shape[1] < 16 * 60 and cache_en["is_final"]:
            if cache_en["start_idx"] == 0:
                return []
            cache_en["tail_chunk"] = True
            feats = cache_en["feats"]
            feats_len = torch.tensor([feats.shape[1]])
            self.asr_model.frontend = None
            results = self.infer(feats, feats_len, cache)
            return results
        else:

 funasr/models/encoder/sanm_encoder.py

@@ -380,7 +380,7 @@
        else:
            xs_pad = self.embed(xs_pad, cache)
        if cache["tail_chunk"]:
            xs_pad = cache["feats"]
            xs_pad = to_device(cache["feats"], device=xs_pad.device)
        else:
            xs_pad = self._add_overlap_chunk(xs_pad, cache)
        encoder_outs = self.encoders0(xs_pad, None, None, None, None)