funasr1.0 streaming

游雁

2024-01-12 bafd056c2831e67aa0f9d8b02832ee160ebb4cc8

funasr1.0 streaming

 examples/industrial_data_pretraining/fsmn_vad_streaming/demo.py

@@ -4,7 +4,8 @@
#  MIT License  (https://opensource.org/licenses/MIT)

from funasr import AutoModel
wav_file = "/Users/zhifu/funasr_github/test_local/asr_example.wav"
wav_file = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav"

chunk_size = 60000 # ms
model = AutoModel(model="/Users/zhifu/Downloads/modelscope_models/speech_fsmn_vad_zh-cn-16k-common-streaming", model_revision="v2.0.0")

@@ -14,23 +15,23 @@
print(res)


#
# import soundfile
# import os
#
# # wav_file = os.path.join(model.model_path, "example/vad_example.wav")
# speech, sample_rate = soundfile.read(wav_file)
#
# chunk_stride = int(chunk_size * 16000 / 1000)
#
# cache = {}
#
# for i in range(int(len((speech)-1)/chunk_stride+1)):
#     speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
#     is_final = i == int(len((speech)-1)/chunk_stride+1)
#     res = model(input=speech_chunk,
#                 cache=cache,
#                 is_final=is_final,
#                 chunk_size=chunk_size,
#                 )
#     print(res)

import soundfile
import os

wav_file = os.path.join(model.model_path, "example/vad_example.wav")
speech, sample_rate = soundfile.read(wav_file)

chunk_stride = int(chunk_size * 16000 / 1000)

cache = {}

for i in range(int(len((speech)-1)/chunk_stride+1)):
    speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
    is_final = i == int(len((speech)-1)/chunk_stride+1) - 1
    res = model(input=speech_chunk,
                cache=cache,
                is_final=is_final,
                chunk_size=chunk_size,
                )
    print(res)

 examples/industrial_data_pretraining/paraformer_streaming/demo.py

@@ -31,7 +31,7 @@

for i in range(int(len((speech)-1)/chunk_stride+1)):
    speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
    is_final = i == int(len((speech)-1)/chunk_stride+1)
    is_final = i == int(len((speech)-1)/chunk_stride+1) - 1
    res = model(input=speech_chunk,
                cache=cache,
                is_final=is_final,

 funasr/frontends/wav_frontend.py

@@ -448,8 +448,8 @@
                feats = torch.stack(cache["lfr_splice_cache"])
                feats_lengths = torch.zeros(batch_size, dtype=torch.int) + feats.shape[1]
                feats, feats_lengths, _ = self.forward_lfr_cmvn(feats, feats_lengths, is_final, cache=cache)
        if is_final:
            self.init_cache(cache)
        # if is_final:
        #     self.init_cache(cache)
        return feats, feats_lengths



 funasr/models/fsmn_vad/model.py

@@ -12,6 +12,7 @@
from funasr.utils.load_utils import load_audio_text_image_video,extract_fbank
from funasr.utils.datadir_writer import DatadirWriter
from torch.nn.utils.rnn import pad_sequence
from funasr.train_utils.device_funcs import to_device

class VadStateMachine(Enum):
    kVadInStateStartPointNotDetected = 1
@@ -580,6 +581,7 @@
            }


            batch = to_device(batch, device=kwargs["device"])
            segments_part, cache = self.forward(**batch)
            if segments_part:
                for batch_num in range(0, batch_size):

 funasr/models/fsmn_vad_streaming/model.py

@@ -587,7 +587,6 @@
                "cache": cache["encoder"]
            }
            segments_i = self.forward(**batch)
            print(segments_i)
            segments.extend(segments_i)