diff --git a/demos/audio_searching/README.md b/demos/audio_searching/README.md index db38d14ed..0fc901432 100644 --- a/demos/audio_searching/README.md +++ b/demos/audio_searching/README.md @@ -226,6 +226,12 @@ recall and elapsed time statistics are shown in the following figure： The retrieval framework based on Milvus takes about 2.9 milliseconds to retrieve on the premise of 90% recall rate, and it takes about 500 milliseconds for feature extraction (testing audio takes about 5 seconds), that is, a single audio test takes about 503 milliseconds in total, which can meet most application scenarios. +* compute embeding takes 500 ms +* retrieval with cosine takes 2.9 ms +* total takes 503 ms + +> test audio is 5 sec + ### 6.Pretrained Models Here is a list of pretrained models released by PaddleSpeech : diff --git a/demos/speaker_verification/README.md b/demos/speaker_verification/README.md index 900b5ae40..55f9a7360 100644 --- a/demos/speaker_verification/README.md +++ b/demos/speaker_verification/README.md @@ -19,6 +19,7 @@ The input of this cli demo should be a WAV file(`.wav`), and the sample rate mus Here are sample files for this demo that can be downloaded: ```bash wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav +wget -c https://paddlespeech.bj.bcebos.com/vector/audio/123456789.wav ``` ### 3. Usage diff --git a/demos/speaker_verification/README_cn.md b/demos/speaker_verification/README_cn.md index f6afa86ac..85224699c 100644 --- a/demos/speaker_verification/README_cn.md +++ b/demos/speaker_verification/README_cn.md @@ -19,6 +19,7 @@ ```bash # 该音频的内容是数字串 85236145389 wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav +wget -c https://paddlespeech.bj.bcebos.com/vector/audio/123456789.wav ``` ### 3. 使用方法 - 命令行 (推荐使用) diff --git a/demos/speech_web/speech_server/main.py b/demos/speech_web/speech_server/main.py index fde063551..d4750d598 100644 --- a/demos/speech_web/speech_server/main.py +++ b/demos/speech_web/speech_server/main.py @@ -113,8 +113,6 @@ async def speech2textOffline(files: List[UploadFile]): # 返回ASR识别结果 asr_res = chatbot.speech2text(out_file_path) return SuccessRequest(result=asr_res) - # else: - # return ErrorRequest(message="文件不是.wav格式") return ErrorRequest(message="上传文件为空") @@ -433,10 +431,6 @@ async def vpr_recog(request: Request, # Sort results by distance metric, closest distances first res = sorted(res.items(), key=lambda item: item[1][1], reverse=True) return res - # except Exception as e: - - -# return {'status': False, 'msg': e}, 400 @app.post('/vpr/del') diff --git a/examples/voxceleb/sv0/RESULT.md b/examples/voxceleb/sv0/RESULT.md index 56ee887c6..aa55584d2 100644 --- a/examples/voxceleb/sv0/RESULT.md +++ b/examples/voxceleb/sv0/RESULT.md @@ -5,3 +5,7 @@ | Model | Number of Params | Release | Config | dim | Test set | Cosine | Cosine + S-Norm | | --- | --- | --- | --- | --- | --- | --- | ---- | | ECAPA-TDNN | 85M | 0.2.1 | conf/ecapa_tdnn.yaml | 192 | test | 0.8188 | 0.7815| + +> [SpeechBrain result](https://github.com/speechbrain/speechbrain/tree/develop/recipes/VoxCeleb/SpeakerRec#speaker-verification-using-ecapa-tdnn-embeddings): +> EER = 0.90% (voxceleb1 + voxceleb2) without s-norm +> EER = 0.80% (voxceleb1 + voxceleb2) with s-norm. diff --git a/paddlespeech/s2t/models/u2/u2.py b/paddlespeech/s2t/models/u2/u2.py index e19f411cf..813e1e529 100644 --- a/paddlespeech/s2t/models/u2/u2.py +++ b/paddlespeech/s2t/models/u2/u2.py @@ -605,8 +605,8 @@ class U2BaseModel(ASRInterface, nn.Layer): xs: paddle.Tensor, offset: int, required_cache_size: int, - att_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]), - cnn_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]), + att_cache: paddle.Tensor, # paddle.zeros([0, 0, 0, 0]) + cnn_cache: paddle.Tensor, # paddle.zeros([0, 0, 0, 0]) ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]: """ Export interface for c++ call, give input chunk xs, and return output from time 0 to current chunk. diff --git a/paddlespeech/s2t/modules/attention.py b/paddlespeech/s2t/modules/attention.py index bfb3fdbef..92990048d 100644 --- a/paddlespeech/s2t/modules/attention.py +++ b/paddlespeech/s2t/modules/attention.py @@ -86,7 +86,7 @@ class MultiHeadedAttention(nn.Layer): self, value: paddle.Tensor, scores: paddle.Tensor, - mask: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool), + mask: paddle.Tensor, # paddle.ones([0, 0, 0], dtype=paddle.bool) ) -> paddle.Tensor: """Compute attention context vector. Args: @@ -127,14 +127,15 @@ class MultiHeadedAttention(nn.Layer): return self.linear_out(x) # (batch, time1, d_model) - def forward(self, - query: paddle.Tensor, - key: paddle.Tensor, - value: paddle.Tensor, - mask: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool), - pos_emb: paddle.Tensor=paddle.empty([0]), - cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]) - ) -> Tuple[paddle.Tensor, paddle.Tensor]: + def forward( + self, + query: paddle.Tensor, + key: paddle.Tensor, + value: paddle.Tensor, + mask: paddle.Tensor, # paddle.ones([0,0,0], dtype=paddle.bool) + pos_emb: paddle.Tensor, # paddle.empty([0]) + cache: paddle.Tensor # paddle.zeros([0,0,0,0]) + ) -> Tuple[paddle.Tensor, paddle.Tensor]: """Compute scaled dot product attention. Args: query (paddle.Tensor): Query tensor (#batch, time1, size). @@ -243,14 +244,15 @@ class RelPositionMultiHeadedAttention(MultiHeadedAttention): return x - def forward(self, - query: paddle.Tensor, - key: paddle.Tensor, - value: paddle.Tensor, - mask: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool), - pos_emb: paddle.Tensor=paddle.empty([0]), - cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]) - ) -> Tuple[paddle.Tensor, paddle.Tensor]: + def forward( + self, + query: paddle.Tensor, + key: paddle.Tensor, + value: paddle.Tensor, + mask: paddle.Tensor, # paddle.ones([0,0,0], dtype=paddle.bool) + pos_emb: paddle.Tensor, # paddle.empty([0]) + cache: paddle.Tensor # paddle.zeros([0,0,0,0]) + ) -> Tuple[paddle.Tensor, paddle.Tensor]: """Compute 'Scaled Dot Product Attention' with rel. positional encoding. Args: query (paddle.Tensor): Query tensor (#batch, time1, size). diff --git a/paddlespeech/s2t/modules/conformer_convolution.py b/paddlespeech/s2t/modules/conformer_convolution.py index 1cc0ab1d5..b35fea5b9 100644 --- a/paddlespeech/s2t/modules/conformer_convolution.py +++ b/paddlespeech/s2t/modules/conformer_convolution.py @@ -108,8 +108,8 @@ class ConvolutionModule(nn.Layer): def forward( self, x: paddle.Tensor, - mask_pad: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool), - cache: paddle.Tensor=paddle.zeros([0, 0, 0]), + mask_pad: paddle.Tensor, # paddle.ones([0,0,0], dtype=paddle.bool) + cache: paddle.Tensor # paddle.zeros([0,0,0,0]) ) -> Tuple[paddle.Tensor, paddle.Tensor]: """Compute convolution module. Args: diff --git a/paddlespeech/s2t/modules/decoder_layer.py b/paddlespeech/s2t/modules/decoder_layer.py index 37b124e84..c8843b723 100644 --- a/paddlespeech/s2t/modules/decoder_layer.py +++ b/paddlespeech/s2t/modules/decoder_layer.py @@ -121,11 +121,16 @@ class DecoderLayer(nn.Layer): if self.concat_after: tgt_concat = paddle.cat( - (tgt_q, self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)[0]), dim=-1) + (tgt_q, self.self_attn(tgt_q, tgt, tgt, tgt_q_mask, + paddle.empty([0]), + paddle.zeros([0, 0, 0, 0]))[0]), + dim=-1) x = residual + self.concat_linear1(tgt_concat) else: x = residual + self.dropout( - self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)[0]) + self.self_attn(tgt_q, tgt, tgt, tgt_q_mask, + paddle.empty([0]), paddle.zeros([0, 0, 0, 0]))[ + 0]) if not self.normalize_before: x = self.norm1(x) @@ -134,11 +139,15 @@ class DecoderLayer(nn.Layer): x = self.norm2(x) if self.concat_after: x_concat = paddle.cat( - (x, self.src_attn(x, memory, memory, memory_mask)[0]), dim=-1) + (x, self.src_attn(x, memory, memory, memory_mask, + paddle.empty([0]), + paddle.zeros([0, 0, 0, 0]))[0]), + dim=-1) x = residual + self.concat_linear2(x_concat) else: x = residual + self.dropout( - self.src_attn(x, memory, memory, memory_mask)[0]) + self.src_attn(x, memory, memory, memory_mask, + paddle.empty([0]), paddle.zeros([0, 0, 0, 0]))[0]) if not self.normalize_before: x = self.norm2(x) diff --git a/paddlespeech/s2t/modules/encoder.py b/paddlespeech/s2t/modules/encoder.py index e5dab1fae..cf4e32fa4 100644 --- a/paddlespeech/s2t/modules/encoder.py +++ b/paddlespeech/s2t/modules/encoder.py @@ -175,7 +175,9 @@ class BaseEncoder(nn.Layer): decoding_chunk_size, self.static_chunk_size, num_decoding_left_chunks) for layer in self.encoders: - xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) + xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad, + paddle.zeros([0, 0, 0, 0]), + paddle.zeros([0, 0, 0, 0])) if self.normalize_before: xs = self.after_norm(xs) # Here we assume the mask is not changed in encoder layers, so just @@ -188,9 +190,9 @@ class BaseEncoder(nn.Layer): xs: paddle.Tensor, offset: int, required_cache_size: int, - att_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]), - cnn_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]), - att_mask: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool), + att_cache: paddle.Tensor, # paddle.zeros([0,0,0,0]) + cnn_cache: paddle.Tensor, # paddle.zeros([0,0,0,0]), + att_mask: paddle.Tensor, # paddle.ones([0,0,0], dtype=paddle.bool) ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]: """ Forward just one chunk Args: @@ -253,6 +255,7 @@ class BaseEncoder(nn.Layer): xs, att_mask, pos_emb, + mask_pad=paddle.ones([0, 0, 0], dtype=paddle.bool), att_cache=att_cache[i:i + 1] if elayers > 0 else att_cache, cnn_cache=cnn_cache[i:i + 1] if paddle.shape(cnn_cache)[0] > 0 else cnn_cache, ) @@ -325,7 +328,8 @@ class BaseEncoder(nn.Layer): chunk_xs = xs[:, cur:end, :] (y, att_cache, cnn_cache) = self.forward_chunk( - chunk_xs, offset, required_cache_size, att_cache, cnn_cache) + chunk_xs, offset, required_cache_size, att_cache, cnn_cache, + paddle.ones([0, 0, 0], dtype=paddle.bool)) outputs.append(y) offset += y.shape[1] diff --git a/paddlespeech/s2t/modules/encoder_layer.py b/paddlespeech/s2t/modules/encoder_layer.py index 5f810dfde..4555b535f 100644 --- a/paddlespeech/s2t/modules/encoder_layer.py +++ b/paddlespeech/s2t/modules/encoder_layer.py @@ -76,9 +76,10 @@ class TransformerEncoderLayer(nn.Layer): x: paddle.Tensor, mask: paddle.Tensor, pos_emb: paddle.Tensor, - mask_pad: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool), - att_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]), - cnn_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]), + mask_pad: paddle. + Tensor, # paddle.ones([0, 0, 0], dtype=paddle.bool) + att_cache: paddle.Tensor, # paddle.zeros([0, 0, 0, 0]) + cnn_cache: paddle.Tensor, # paddle.zeros([0, 0, 0, 0]) ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]: """Compute encoded features. Args: @@ -105,7 +106,8 @@ class TransformerEncoderLayer(nn.Layer): if self.normalize_before: x = self.norm1(x) - x_att, new_att_cache = self.self_attn(x, x, x, mask, cache=att_cache) + x_att, new_att_cache = self.self_attn( + x, x, x, mask, paddle.empty([0]), cache=att_cache) if self.concat_after: x_concat = paddle.concat((x, x_att), axis=-1) @@ -193,9 +195,9 @@ class ConformerEncoderLayer(nn.Layer): x: paddle.Tensor, mask: paddle.Tensor, pos_emb: paddle.Tensor, - mask_pad: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool), - att_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]), - cnn_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]), + mask_pad: paddle.Tensor, #paddle.ones([0, 0, 0],dtype=paddle.bool) + att_cache: paddle.Tensor, # paddle.zeros([0, 0, 0, 0]) + cnn_cache: paddle.Tensor, # paddle.zeros([0, 0, 0, 0]) ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]: """Compute encoded features. Args: diff --git a/paddlespeech/server/engine/asr/online/python/asr_engine.py b/paddlespeech/server/engine/asr/online/python/asr_engine.py index 269a33ba7..87d88ee60 100644 --- a/paddlespeech/server/engine/asr/online/python/asr_engine.py +++ b/paddlespeech/server/engine/asr/online/python/asr_engine.py @@ -476,8 +476,12 @@ class PaddleASRConnectionHanddler: # forward chunk (y, self.att_cache, self.cnn_cache) = self.model.encoder.forward_chunk( - chunk_xs, self.offset, required_cache_size, self.att_cache, - self.cnn_cache) + chunk_xs, + self.offset, + required_cache_size, + att_cache=self.att_cache, + cnn_cache=self.cnn_cache, + att_mask=paddle.ones([0, 0, 0], dtype=paddle.bool)) outputs.append(y) # update the global offset, in decoding frame unit